AI Insight
Researchers developed MediRec, a large language model-based framework designed to recommend medications from Chinese electronic health records while providing explainable clinical reasoning. The system combines reasoning-chain distillation with reinforcement learning to improve both accuracy and interpretability, achieving an F1 score of 0.5813 and Jaccard score of 0.4626 on a Chinese medication recommendation benchmark. MediRec addresses a gap in applying AI to Chinese clinical settings, where existing approaches have primarily focused on English datasets and less interpretable code-based predictions.
Why it matters
This framework could support clinical decision-making in Chinese healthcare settings by providing both medication recommendations and transparent explanations for those recommendations. The explainability component is particularly important for building physician trust and enabling verification of AI-assisted medical decisions in real-world clinical practice.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
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Abstract: 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.
Source: MediRec: Enhancing Chinese Medication Recommendation with Explainable Clinical Reasoning