Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)

Xinyu Qin 秦兴宇, Lu Wang, Martin Katzman
January 2025

Abstract

This paper uses SHAP and counterfactual explanation to analyze dementia risk in diabetic and non-diabetic populations..

Type
Journal-Article
Publication
Submitted to Journal of the American Medical Informatics Association (JAMIA)
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Explainable AI Depression Counterfactual Reasoning
Xinyu Qin 秦兴宇
Xinyu Qin 秦兴宇
Phd student of Biomedical Engineering

My research interests include computing and programmable matter.

Lu Wang
Lu Wang
Assistant Professor of Biomedical Engineering

My research interests are developing and applying Machine Learning, Data Mining and Statistical methods (e.g., Multi-task Learning, Survival Analysis, Clustering, Risk Factor Analysis and Causal Discovery) on various data including gene expression, electronic health/medical records, and DNA sequencing reads for both cognitive disorders (e.g., delirium, Alzheimer’s disease, dementia, major depressive disorder) and chronic diseases (e.g., cancer, obesity, hypertension). Inspired by the human factors approach, she also designs and develops Human-Centered Artificial Intelligence tools for users to integrate, visualize, analyze, and interpret health data in order to improve the interoperability and accessibility of AI-assisted healthcare decision support.