Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings
Abstract
This manuscript estimates treatment effects for depression under longitudinal therapy switching settings.
Xinyu Qin, Martin Katzman, Alexandria Greifenberger, Elssa Toumeh, Sachinthya Lokuge, Tia Sternat, Ruiheng Yu, and Lu Wang. Estimating Treatment Effects for Depression in Longitudinal Therapy Switching Settings. Manuscript submitted to the Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2026), 2026.
Xinyu Qin
PhD Student of Biomedical Engineering
My research interests include Causal Inference, Survival Analysis, and Digital Twin.
Ruiheng Yu
PhD Student of Biomedical Engineering
My research interests include Computer vision, explainable AI, LLM in health area.
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.