Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population)
Abstract
This paper presents explainable counterfactual reasoning for depression medication selection at personalized and population levels.
X. Qin, M. H. Chignell, A. Greifenberger, S. Lokuge, E. Toumeh, T. Sternat, M. Katzman, and L. Wang. Explainable Counterfactual Reasoning in Depression Medication Selection at Multi-Levels (Personalized and Population). BMC Medical Informatics and Decision Making, published online March 4, 2026.
Xinyu Qin
PhD Student of Biomedical Engineering
My research interests include Causal Inference, Survival Analysis, and Digital Twin.
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.