Personalized and Population-Level Explainable AI for Dementia Risk Analysis and Intervention Strategies in Aging Diabetic and Non-Diabetic Populations
Abstract
This paper uses SHAP and counterfactual explanation to analyze dementia risk in diabetic and non-diabetic populations..
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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.
Ruiheng Yu 余睿恒
Phd student of Biomedical Engineering
My research interests include Computer vision, explainable AI, LLM in health area.