Medication recommendation systems are designed to deliver personalized drug suggestions that are closely aligned with individual patient needs. Previous studies have primarily concentrated on developing medication embeddings, achieving significant progress. Nonetheless, these approaches often fall short in accurately reflecting individual patient profiles, mainly due to challenges in distinguishing between various patient conditions and the inability to establish precise correlations between specific conditions and appropriate medications. In response to these issues, we introduce DisMed, a model that focuses on patient conditions to enhance personalization. DisMed employs causal inference to discern clear, quantifiable causal links. It then examines patient conditions in depth, recognizing and adapting to the evolving nuances of these conditions, and mapping them directly to corresponding medications. Additionally, DisMed leverages data from multiple patient visits to propose combinations of medications. Comprehensive testing on real-world datasets demonstrates that DisMed not only improves the customization of patient profiles but also surpasses leading models in both precision and safety.

翻译：药物推荐系统旨在提供与个体患者需求紧密对齐的个性化用药建议。以往研究主要集中于开发药物嵌入表示，并取得了显著进展。然而，这些方法往往难以准确反映个体患者的特征，主要原因在于难以区分不同的患者状况，且无法建立特定状况与合适药物之间的精确关联。针对这些问题，我们提出了DisMed模型，该模型聚焦于患者状况以增强个性化。DisMed采用因果推断来揭示清晰、可量化的因果关系。接着，它深入分析患者状况，识别并适应这些状况的演变细微差别，将其直接映射到相应药物。此外，DisMed利用多次患者就诊的数据提出药物组合方案。在真实世界数据集上的全面测试表明，DisMed不仅提升了患者档案的定制化程度，而且在精确性和安全性方面均超越了领先模型。