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 regeneration mechanism for complex clinical questions, which leverages ground-truth answers as hints ...
| Authors | Yushi Cao, Yiming Chen, Hongchao Jiang et al. |
|---|---|
| Primary category | cs.CL |
| Published | 2 May 2026 |
| Last updated at source | 2 May 2026 |
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