Historical prescriptions and selected candidate drugs relevant to the current visit serve as important references for medication recommendation. However, in the absence of explicit intrinsic principles for semantic composition, existing methods treat synergistic drugs as independent entities and fail to capture their collective therapeutic effects, resulting in a mismatch between medication-level references and longitudinal patient representations. In this paper, we propose MSAM, a novel medication recommendation model that bridges the gap via multi-level medication abstraction. The model introduces a multi-head graph reasoning mechanism to organize flat daily medication sets into clinically meaningful semantic units, serving as intermediate abstraction results to propagate features from individual drugs to higher-level representations. Building on these units, MSAM performs two-stage abstraction over historical prescriptions and selected candidates via intra- and inter-level feature propagation across heterogeneous clinical structures, capturing collective therapeutic effects aligned with patient conditions. Experiments on two real-world clinical datasets show that MSAM consistently outperforms state-of-the-art methods, validating the effectiveness of structural medication abstraction for recommendation.

翻译：历史处方和与当前就诊相关的候选药物为用药推荐提供了重要参考。然而，由于缺乏明确的语义组合内在原则，现有方法将具有协同作用的药物视为独立实体，无法捕捉其集体治疗效果，导致药物级参考与纵向患者表征之间存在不匹配。本文提出MSAM，一种通过多层次药物抽象来弥合这一差距的新型用药推荐模型。该模型引入多头图推理机制，将扁平的每日药物集合组织成具有临床意义的语义单元，作为中间抽象结果将特征从单个药物传播到更高层次表征。基于这些单元，MSAM通过跨异构临床结构的层内与层间特征传播，对历史处方和候选药物进行两阶段抽象，从而捕捉与患者状况相符的集体治疗效果。在两个真实世界临床数据集上的实验表明，MSAM持续优于现有最先进方法，验证了结构化药物抽象对推荐的有效性。