ERSW-project

This is the code repository for the article: An interpretable early dynamic sequential predictor for sepsis-induced coagulopathy progression in the real-world using AI, which is published in Frontiers in Medicine.

1. Here are some results of the article.

2. This is the code structure of ERSW-project-main.

• Code structure

  ERSW-project-master

  data

  BIDMC set and XJTUMC set

  lib

  deep_learning_model.py
  machine_learning_model.py
  figure_plotting.py
  shap_plotting.py

  utils

  data_dividation.py
  get_sample.py
  merge_annotate.py
  pre_annotation.py

  main.py

3. The repository provides the code of our research reproduction as follows:

• Install necessary Python dependencies, such as "torch", 'shap', and so on.
• Acquire or generate the necessary Dataset which are used for analysis by the following ways.
  • BIDMC dataset were obtained from the MIMIC-III database, which can be downloaded from https://mimic.mit.edu/iii.
  • XJTUMC dataset were obtained from the Biobank of First Affiliated Hospital of Xi’an Jiaotong University, which is a restricted-access resource and is only available by submitting a request to the author and the institution. You can send your request to the email: [email protected]
• Run main.py to deal with the data and develop the model for predicting the coagulopathy.