Reinforcement Learning enhanced Online Adaptive Clinical Decision Support via Digital Twin powered Policy and Treatment Effect optimized Reward

Abstract

This paper presents a reinforcement learning approach enhanced with digital twin technology for online adaptive clinical decision support, optimizing treatment policies and effects.

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.