Medication recommendation is a fundamental yet crucial branch of healthcare that presents opportunities to assist physicians in making more accurate medication prescriptions for patients with complex health conditions. Previous studies have primarily focused on learning patient representation from electronic health records (EHR). While considering the clinical manifestations of the patient is important, incorporating domain-specific prior knowledge is equally significant in diagnosing the patient's health conditions. However, effectively integrating domain knowledge with the patient's clinical manifestations can be challenging, particularly when dealing with complex clinical manifestations. Therefore, in this paper, we first identify comprehensive domain-specific prior knowledge, namely the Unified Medical Language System (UMLS), which is a comprehensive repository of biomedical vocabularies and standards, for knowledge extraction. Subsequently, we propose a knowledge injection module that addresses the effective integration of domain knowledge with complex clinical manifestations, enabling an effective characterization of the health conditions of the patient. Furthermore, considering the significant impact of a patient's medication history on their current medication, we introduce a historical medication-aware patient representation module to capture the longitudinal influence of historical medication information on the representation of current patients. Extensive experiments on three publicly benchmark datasets verify the superiority of our proposed method, which outperformed other methods by a significant margin. The code is available at: https://github.com/sherry6247/DKINet.

翻译：用药推荐是医疗保健中一个基础而关键的分支，旨在辅助医生为复杂病情的患者制定更精准的用药方案。以往研究主要侧重于从电子健康记录中学习患者表征。尽管考虑患者的临床表现至关重要，但融入领域特有的先验知识对诊断患者的健康状况同样重要。然而，如何有效整合领域知识与患者临床表现仍具挑战性，尤其是在处理复杂临床表现时。因此，本文首先识别了全面的领域特有先验知识——统一医学语言系统（UMLS，即生物医学词汇与标准的综合知识库）用于知识提取。随后，我们提出知识注入模块，该模块解决了领域知识与复杂临床表现的有效整合问题，从而实现对患者健康状况的精准表征。此外，考虑到患者用药史对其当前用药的显著影响，我们引入历史用药感知的患者表征模块，以捕捉历史用药信息对当前患者表征的纵向影响。在三个公开基准数据集上的大量实验验证了所提方法的优越性，其性能显著优于其他方法。代码开源地址：https://github.com/sherry6247/DKINet。