ReMedi: Reasoner for Medical Clinical Prediction

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 re-generation mechanism for complex clinical questions, which leverages ground-truth answers as hints to enhance reasoning for further supervised fine-tuning and preference tuning. ReMedi integrates groundtruth outcome guidance into the preference data construction loop, regenerating rationaleanswer variants. By tuning on these rationaleanswer pairs, the model improves its predictive performance on clinical prediction tasks. Experiments on multiple EHR prediction tasks demonstrate substantial gains of up to 19.9% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi's effectiveness in real-world clinical prediction.