发表机构
South-Central Minzu University（中南民族大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对现有药物推荐忽略分子结构和ATC层级的问题，提出HADRec框架，融合分子知识与EHR，采用层级预测器与一致性约束，在MIMIC-III和IV上达到最优性能，且校准良好、推理临床对齐。
AI 中文摘要
准确的药物推荐是临床决策的核心，直接决定治疗效果和患者安全。然而，现有方法存在两个关键局限：药物常被抽象为离散标记，忽略了其分子结构和药理机制；且常用的“扁平”推荐范式未能利用国际标准化的解剖治疗化学（ATC）分类系统的层级逻辑。为解决这些问题，我们提出了HADRec，一种层级感知的药物推荐框架，将分子知识与电子健康记录（EHR）相结合。HADRec使用LLaMA-7B编码临床笔记以获取丰富的患者表示，并使用ChemBERTa编码药物的简化分子输入线性进入系统字符串，构建全局分子知识库。随后，交叉注意力机制在患者状态和药物特征之间进行深度多模态融合。该框架进一步整合了层级预测器和一种新颖的一致性约束损失，以强制严格遵循ATC逻辑依赖。在MIMIC-III上的大量实验表明，HADRec在Jaccard、F1和PR-AUC指标上达到了最先进的性能。在MIMIC-IV上的外部验证证实了在分布偏移下的强泛化能力，校准分析显示在MIMIC-IV上预测置信度校准良好，ECE=0.04，Brier=0.06。反事实评估揭示了临床对齐的推理，将疾病特异性治疗与一般护理区分开来。这些结果共同确立了HADRec作为一条高性能、可解释且临床基础扎实的路径，通向安全可靠的AI驱动药物推荐。
英文摘要
Accurate medication recommendation is central to clinical decision-making, directly determining therapeutic efficacy and patient safety. However, existing methods suffer from two key limitations: drugs are often abstracted as discrete tokens, ignoring their molecular structures and pharmacological mechanisms, and the commonly used "flat" recommendation paradigm fails to leverage the hierarchical logic of the internationally standardized Anatomical Therapeutic Chemical (ATC) classification system. To address these issues, we propose HADRec, a Hierarchy-Aware Drug Recommendation framework that integrates molecular knowledge with electronic health records (EHRs). HADRec employs LLaMA-7B to encode clinical notes for rich patient representations and ChemBERTa to encode drug Simplified Molecular Input Line Entry System strings, building a global molecular knowledge base. A cross-attention mechanism then performs deep multimodal fusion between patient states and drug features. The framework further incorporates a hierarchical predictor and a novel consistency constraint loss to enforce strict adherence to ATC logical dependencies. Extensive experiments on MIMIC-III demonstrate that HADRec achieves state-of-the-art performance across Jaccard, F1, and PR-AUC. External validation on MIMIC-IV confirms strong generalization under distribution shifts, and calibration analysis shows well-calibrated predictive confidence on MIMIC-IV with ECE = 0.04, and Brier = 0.06. Counterfactual evaluation reveals clinically aligned reasoning, disentangling disease-specific treatments from general care. Together, these results establish HADRec as a high-performance, interpretable, and clinically grounded pathway toward safe and reliable AI-driven medication recommendation.
