4 papers
Learning Preference-Based Objectives from Clinical Narratives for Dynamic Sepsis Treatment
Daniel J. Tan, Jayne Hui Zhen Chan, Kai Wen Hwang +3
Designing reward functions for reinforcement learning (RL) in healthcare remains challenging because clinically meaningful outcomes are sparse, delayed, and difficult to explicitly…
DeepEN: A Deep Reinforcement Learning Framework for Personalized Enteral Nutrition in Critical Care
Daniel Jason Tan, Jiayang Chen, Dilruk Perera +2
Objective: Enteral nutrition (EN) delivery in the ICU remains suboptimal due to limited personalization and uncertainty regarding appropriate calorie, protein, and fluid targets un…
medDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support
Qianyi Xu, Gousia Habib, Feng Wu +2
Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses can vary significantly and evolve over time. Clinical d…
Advancing Multi-Organ Disease Care: A Hierarchical Multi-Agent Reinforcement Learning Framework
Daniel J. Tan, Qianyi Xu, Kay Choong See +2
In healthcare, multi-organ system diseases pose unique and significant challenges as they impact multiple physiological systems concurrently, demanding complex and coordinated trea…