Medication recommendation is a fundamental yet crucial branch of healthcare, which provides opportunities to support clinical physicians with more accurate medication prescriptions for patients with complex health conditions. Learning from electronic health records (EHR) to recommend medications is the most common way in previous studies. However, most of them neglect incorporating domain knowledge according to the clinical manifestations in the EHR of the patient. To address these issues, we propose a novel \textbf{D}omain \textbf{K}nowledge \textbf{I}nformed \textbf{Net}work (DKINet) to integrate domain knowledge with observable clinical manifestations of the patient, which is the first dynamic domain knowledge informed framework toward medication recommendation. In particular, we first design a knowledge-driven encoder to capture the domain information and then develop a data-driven encoder to integrate domain knowledge into the observable EHR. To endow the model with the capability of temporal decision, we design an explicit medication encoder for learning the longitudinal dependence of the patient. Extensive experiments on three publicly available datasets verify the superiority of our method. The code will be public upon acceptance.

翻译：用药推荐是医疗健康领域中一项基础且关键的任务，能够帮助临床医生为患有复杂健康状况的患者提供更精确的用药方案。以往的研究中最常见的方法是学习电子健康记录（EHR）以推荐药物。然而，大多数方法未能根据患者EHR中的临床表现整合领域知识。为解决这些问题，我们提出了一种新颖的\textbf{领域知识赋能网络}（DKINet），将领域知识与患者可观察的临床表现相结合，这是首个面向用药推荐的动态领域知识赋能框架。具体而言，我们首先设计了一个知识驱动编码器以捕获领域信息，随后开发了一个数据驱动编码器以将领域知识整合到可观察的EHR中。为赋予模型时间决策能力，我们设计了一个显式用药编码器以学习患者的纵向依赖关系。在三个公开数据集上的大量实验验证了本方法的优越性。代码将在接收后公开。