用可解释推理提升中文用药推荐准确率
MediRec: Enhancing Chinese Medication Recommendation with Explainable Clinical Reasoning
- 结合临床推理链蒸馏与强化学习，增强模型可解释性
- 在中文数据集上达到F1 0.5813、Jaccard 0.4626
- 适合需要透明决策的中文医疗AI场景
大型语言模型（LLMs）在临床决策支持中展现出强大的语言理解与推理能力，但在中文临床用药推荐领域仍缺乏深入探索。现有方法多基于英文电子健康记录数据集，聚焦粗粒度药物代码预测，难以支持可解释的临床决策。本文提出MediRec，一种基于LLM的可解释中文用药推荐框架。该框架融合临床导向的推理链蒸馏与强化学习，同时提升推荐准确率与可解释性。在中文用药推荐基准上的全面实验表明，MediRec取得优异性能，F1得分为0.5813，Jaccard得分为0.4626。进一步分析显示，MediRec生成的推荐具有临床合理性且推理过程透明，验证了其在中文医疗环境中可解释用药决策支持的有效性。
原文摘要 · Abstract (English)
Large language models (LLMs) have shown strong potential for clinical decision support through their advanced language understanding and reasoning capabilities. However, their application to Chinese clinical medication recommendation remains largely unexplored. Existing approaches are primarily developed on English electronic health record datasets and focus on coarse-grained medication code prediction, offering limited support for interpretable clinical decision-making. In this work, we propose MediRec, an explainable LLM-based framework for Chinese medication recommendation from electronic health records. MediRec combines clinically grounded reasoning-chain distillation with reinforcement learning to improve both recommendation accuracy and interpretability. Comprehensive experiments on a Chinese medication recommendation benchmark show that MediRec achieves strong performance, with an F1 score of 0.5813 and a Jaccard score of 0.4626. Further analyses indicate that MediRec generates clinically plausible recommendations with transparent reasoning, demonstrating its effectiveness for explainable medication decision support in Chinese healthcare settings.
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