Medication recommendations aim to generate safe and effective medication sets from health records. However, accurately recommending medications hinges on inferring a patient's latent clinical condition from sparse and noisy observations, which requires both (i) preserving the visit-level combinatorial semantics of co-occurring entities and (ii) leveraging informative historical references through effective, visit-conditioned retrieval. Most existing methods fall short in one of both aspects: graph-based modeling often fragments higher-order intra-visit patterns into pairwise relations, while inter-visit augmentation methods commonly exhibit an imbalance between learning a globally stable representation space and performing dynamic retrieval within it. To address these limitations, this paper proposes HypeMed, a two-stage hypergraph-based framework unifying intra-visit coherence modeling and inter-visit augmentation. HypeMed consists of two core modules: MedRep for representation pre-training, and SimMR for similarity-enhanced recommendation. In the first stage, MedRep encodes clinical visits as hyperedges via knowledge-aware contrastive pre-training, creating a globally consistent, retrieval-friendly embedding space. In the second stage, SimMR performs dynamic retrieval within this space, fusing retrieved references with the patient's longitudinal data to refine medication prediction. Evaluation on real-world benchmarks shows that HypeMed outperforms state-of-the-art baselines in both recommendation precision and DDI reduction, simultaneously enhancing the effectiveness and safety of clinical decision support.

翻译：用药推荐旨在从健康记录中生成安全有效的用药方案。然而，准确推荐药物依赖于从稀疏且有噪声的观测中推断患者的潜在临床状况，这需要同时具备两点：(i) 保留就诊层级中实体共现的组合语义；(ii) 通过有效的、基于就诊条件的检索，利用信息丰富的历史参考。现有方法大多在以下两方面存在不足：基于图的建模常将高阶的就诊内模式拆解为成对关系，而就诊间增强方法通常在学习全局稳定的表示空间与在该空间内进行动态检索之间表现出不平衡。为解决这些局限性，本文提出HypeMed，一种统一就诊内一致性建模与就诊间增强的两阶段超图框架。HypeMed包含两个核心模块：用于表示预训练的MedRep和用于相似性增强推荐的SimMR。在第一阶段，MedRep通过知识感知的对比预训练将临床就诊编码为超边，构建全局一致且便于检索的嵌入空间。在第二阶段，SimMR在此空间内进行动态检索，将检索到的参考信息与患者的纵向数据融合，以优化药物预测。在真实世界基准上的评估表明，HypeMed在推荐精度和药物相互作用降低方面均优于现有最先进的基线方法，同时增强了临床决策支持的有效性和安全性。