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

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