Merge pull request #15 from andreasgrv/corponi2024tp

Add Filippo's paper

_publications/corponi2024tp.md

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+---
+collection: publications
+ref: "corponi2024tp"
+permalink: "publications/corponi2024tp"
+title:  "Automated mood disorder symptoms monitoring from multivariate time-series sensory data: getting the full picture beyond a single number"
+date:   2024-10-04 00:00
+tags: wearables time_series mental_health
+image: "/images/papers/corponi2024tp/pipeline.png"
+spotlight: "/images/papers/corponi2024tp/pipeline-spotlight.png"
+authors: "Filippo Corponi, Bryan M. Li, Gerard Anmella, Ariadna Mas, Isabella Pacchiarotti, Marc Valentí, Iria Grande, Antoni Benabarre, Marina Garriga, Eduard Vieta, Stephen M. Lawrie, Heather C. Whalley, Diego Hidalgo-Mazzei, Antonio Vergari"
+paperurl: https://www.nature.com/articles/s41398-024-02876-1
+pdf: "https://www.nature.com/articles/s41398-024-02876-1"
+venue: "Nature Translational Psychiatry"
+code: "https://github.com/april-tools/wear-your-scales"
+excerpt: "A novel task in remote sensing for mood disorders, better aligned with the real-world clinical practice, beyond a reductioninst acute illness yes-no binary classification: old and new machine learning challenges"
+abstract: "Mood disorders (MDs) are among the leading causes of disease burden worldwide. Limited specialized care availability remains a major bottleneck thus hindering pre-emptive interventions. MDs manifest with changes in mood, sleep, and motor activity, observable in ecological physiological recordings thanks to recent advances in wearable technology. Therefore, near-continuous and passive collection of physiological data from wearables in daily life, analyzable with machine learning (ML), could mitigate this problem, bringing MDs monitoring outside the clinician’soffice. Previous works predict a single label, either the disease state or a psychometric scale total score. However, clinical practice suggests that the same label may underlie different symptom profiles, requiring specific treatments. Here we bridge this gap by proposing a new task: inferring all items in HDRS and YMRS, the two most widely used standardized scales for assessing MDs symptoms, using physiological data from wearables. To that end, we develop a deep learning pipeline to score the symptoms of a large cohort of MD patients and show that agreement between predictions and assessments by an expert clinician is clinically significant (quadratic Cohen’s κ and macro-average F1 score both of 0.609). While doing so, we investigate several solutions to the ML challenges associated with this task, including multi-task learning, class imbalance, ordinal target variables, and subject-invariant representations. Lastly, we illustrate the importance of testing on out-of distribution samples."
+supplemental: "https://www.nature.com/articles/s41398-024-02876-1#Sec21"
+bibtex: "@article{corponi2024automated,
+  title={Automated mood disorder symptoms monitoring from multivariate time-series sensory data: Getting the full picture beyond a single number},
+  author={Corponi, Filippo and Li, Bryan M and Anmella, Gerard and Mas, Ariadna and Pacchiarotti, Isabella and Valentí, Marc and Grande, Iria and Benabarre, Antoni and Garriga, Marina and Vieta, Eduard and others},
+  journal={Translational Psychiatry},
+  volume={14},
+  number={1},
+  pages={161},
+  year={2024},
+  publisher={Nature Publishing Group UK London}
+}"
+---