Portrait of Ruilin Wang

Ruilin Wang

ruilin.wang@mila.quebec
Master's Research - McGill University
Supervisor
Yue Li
Research Topics
AI in Health
Applied Machine Learning
Deep Learning
Large Language Models (LLM)
Multi-Agent Systems
GitHub

Publications

FedEHR-Agents: Federated Agentic Optimization for Automated EHR Modeling
Jun Bai
Ruilin Wang
Yue Li
Recent advances in large language models are enabling autonomous clinical agents to perform increasingly complex electronic health record (E… (see more)HR) modeling workflows. However, agents deployed at individual hospitals remain constrained by institution-specific data and modeling environments, while direct cross-hospital collaboration is restricted by the sensitivity of patient-level EHR data. Although federated learning (FL) provides a natural foundation for privacy-preserving collaboration, existing approaches remain predominantly model-centric, limiting federation to prediction models or their updates while overlooking the richer modeling experience accumulated by autonomous agents. To address this limitation, we propose FedEHR-Agents, an experience-centric federated agentic optimization framework for automated EHR modeling. Each hospital deploys an autonomous clinical EHR agent that performs data preprocessing and model development while refining local clinical modeling experience through historical memory, task-specific evaluation, and TextGrad-based prompt refinement. The federated server performs evidence-guided experience aggregation to integrate reliable and complementary modeling experience across heterogeneous hospitals and distills the aggregated experience into global meta-prompts for subsequent local refinement. Extensive experiments on real-world multi-hospital EHR benchmarks demonstrate that FedEHR-Agents consistently outperforms local and federated baselines across diverse clinical prediction tasks and remains robust across different federation scales and LLM backbones. These results establish clinical modeling experience as a promising collaborative object beyond conventional parameter-centric FL and point toward federated autonomous clinical intelligence.
2026-08-27
arXiv (preprint)
doi.org
arxiv.org
DoctorAgents: an agentic framework to iteratively refine AutoML pipeline for small clinical temporal data
Ruilin Wang
Bo-Hong Wang
Elizabeth Kourbatski
Jun Bai
Hegang Chen
Ziyang Song
Gilles Boire
Marie Hudson
Yue Li
Clinical machine learning (ML) has the potential to support high-stakes medical decision-making, but reliable deployment is often constraine… (see more)d by scarce, heterogeneous, and temporal complexity. Developing effective ML pipelines for such data remains time-consuming and error-prone, while existing automated machine learning (AutoML) systems only partially address this challenge because they largely rely on brute-force search over predefined spaces and lack explicit reasoning and memory. We therefore reformulate AutoML for small clinical data from exhaustive search to reasoning-driven refinement. We propose DoctorAgents, an agentic AI framework that autonomously constructs and optimizes end-to-end ML pipelines through specialized large language model (LLM) agents for generation, validation, and refinement. DoctorAgents backpropagates natural-language feedback through textual gradient descent to perform targeted updates without exhaustive search. Experiments across diverse clinical tasks show that DoctorAgents consistently outperforms established AutoML baselines while producing more interpretable task-specific representations.
2026-08-04
arXiv (preprint)
doi.org
arxiv.org
ChatHealthAI: Aligning Electronic Health Record Representations with Large Language Models for Grounded Clinical Reasoning
Bo-Hong Wang
Baicheng Peng
Ruilin Wang
Jun Bai
Ziyang Song
Yue Li
Large language models (LLMs) exhibit strong natural-language reasoning abilities for clinical decision support, but struggle to effectively … (see more)model structured longitudinal electronic health records (EHRs). In contrast, EHR foundation models can learn predictive patient representations, yet lack interpretable language-based reasoning. To bridge this gap, we propose ChatHealthAI, a multimodal reasoning framework that aligns structured EHR representations from a pretrained EHR foundation model with the semantic space of a frozen LLM through a task-aware resampler. By integrating longitudinal patient representations with refined clinical event descriptions, ChatHealthAI enables clinically grounded natural-language reasoning while maintaining accurate patient prediction. We evaluated ChatHealthAI on three clinical predictive tasks from the EHRSHOT benchmark. Results show that ChatHealthAI improves reasoning quality and interpretability while preserving competitive predictive performance. These findings highlight the potential of integrating EHR foundation models with pretrained LLMs for interpretable clinical prediction.
2026-05-31
arXiv (preprint)
doi.org
arxiv.org