27 citations · 40 across the 9 of their papers we have counts for
10 papers
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…
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…
Adapting an Artificial Intelligence Sexually Transmitted Diseases Symptom Checker Tool for Mpox Detection: The HeHealth Experience
Rayner Kay Jin Tan, Dilruk Perera, Salomi Arasaratnam +1
Artificial Intelligence applications have shown promise in the management of pandemics and have been widely used to assist the identification, classification, and diagnosis of medi…
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,…
Towards Comprehensive Recommender Systems: Time-Aware UnifiedcRecommendations Based on Listwise Ranking of Implicit Cross-Network Data
Dilruk Perera, Roger Zimmermann
The abundance of information in web applications make recommendation essential for users as well as applications. Despite the effectiveness of existing recommender systems, we find…
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…
CnGAN: Generative Adversarial Networks for Cross-network user preference generation for non-overlapped users
Dilruk Perera, Roger Zimmermann
A major drawback of cross-network recommender solutions is that they can only be applied to users that are overlapped across networks. Thus, the non-overlapped users, which form th…