27 citations · 40 across the 9 of their papers we have counts for
7 papers · 1 filter
medR: Reward Engineering for Clinical Offline Reinforcement Learning via Tri-Drive Potential Functions
Qianyi Xu, Gousia Habib, Feng Wu +5
Reinforcement Learning (RL) offers a powerful framework for optimizing dynamic treatment regimes (DTRs). However, clinical RL is fundamentally bottlenecked by reward engineering: t…
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…
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…
LSTM Networks for Online Cross-Network Recommendations
Dilruk Perera, Roger Zimmermann
Cross-network recommender systems use auxiliary information from multiple source networks to create holistic user profiles and improve recommendations in a target network. However,…
Exploring the use of Time-Dependent Cross-Network Information for Personalized Recommendations
Dilruk Perera, Roger Zimmermann
The overwhelming volume and complexity of information in online applications make recommendation essential for users to find information of interest. However, two major limitations…