@inproceedings{cao-etal-2026-remedi,
title = "{R}e{M}edi: Reasoner for Medical Clinical Prediction",
author = "Cao, Yushi and
Chen, Yiming and
Jiang, Hongchao and
Lee, Hung-yi and
Tan, Robby T.",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.1011/",
doi = "10.18653/v1/2026.findings-acl.1011",
pages = "20232--20241",
ISBN = "979-8-89176-395-1",
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{\%} over state-of-the-art baselines in terms of F1 score, underscoring ReMedi{'}s effectiveness in real-world clinical prediction."
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<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% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi’s effectiveness in real-world clinical prediction.</abstract>
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%0 Conference Proceedings
%T ReMedi: Reasoner for Medical Clinical Prediction
%A Cao, Yushi
%A Chen, Yiming
%A Jiang, Hongchao
%A Lee, Hung-yi
%A Tan, Robby T.
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F cao-etal-2026-remedi
%X 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% over state-of-the-art baselines in terms of F1 score, underscoring ReMedi’s effectiveness in real-world clinical prediction.
%R 10.18653/v1/2026.findings-acl.1011
%U https://aclanthology.org/2026.findings-acl.1011/
%U https://doi.org/10.18653/v1/2026.findings-acl.1011
%P 20232-20241
Markdown (Informal)
[ReMedi: Reasoner for Medical Clinical Prediction](https://aclanthology.org/2026.findings-acl.1011/) (Cao et al., Findings 2026)
ACL
- Yushi Cao, Yiming Chen, Hongchao Jiang, Hung-yi Lee, and Robby T. Tan. 2026. ReMedi: Reasoner for Medical Clinical Prediction. In Findings of the Association for Computational Linguistics: ACL 2026, pages 20232–20241, San Diego, California, United States. Association for Computational Linguistics.