ReMedi: Reasoner for Medical Clinical Prediction

TL;DR

ReMedi is a novel framework that enhances clinical outcome prediction from electronic health records by generating rationale-answer pairs to improve reasoning and accuracy.

Contribution

It introduces a sample regeneration mechanism leveraging ground-truth answers to fine-tune models for better clinical prediction performance.

Findings

Achieved up to 19.9% improvement in F1 score over baselines.

Demonstrated effectiveness across multiple EHR prediction tasks.

Utilized rationale-answer pairs for model fine-tuning and preference tuning.

Abstract

Predicting future clinical outcomes from electronic health records (EHR) remains challenging due to the complexity and heterogeneity of patient data. LLMs have shown strong potential for such predictive tasks, yet existing approaches mainly focus on enhancing medical knowledge through distillation or RAG while relying on the model's internal ability to interpret contextual information. In this work, we present ReMedi (Reasoner for Medical Clinical Prediction), a framework for improving clinical outcome prediction from EHR. ReMedi generates rationale-answer pairs using a challenging sample regeneration mechanism for complex clinical questions, which leverages ground-truth answers as hints to enhance reasoning for further fine-tuning and preference tuning. ReMedi integrates ground-truth outcome guidance into the preference data construction loop, regenerating rationale-answer variants. By tuning on these rationale-answer pairs, the model improves its predictive performance. Experiments on multiple EHR prediction tasks demonstrate substantial gains of up to 19.9 percent over state-of-the-art baselines in terms of F1 score, underscoring ReMedi's effectiveness in real-world clinical prediction.