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…
Beyond Prediction: Reinforcement Learning as the Defining Leap in Healthcare AI
Dilruk Perera, Gousia Habib, Qianyi Xu +4
Reinforcement learning (RL) marks a fundamental shift in how artificial intelligence is applied in healthcare. Instead of merely predicting outcomes, RL actively decides interventi…
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…