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一个基础且关键的分支，它为临床医生针对复杂健康状况的患者提供更精确的用药处方提供了支持。从电子健康记录（EHR）中学习以推荐药物是既往研究中最常见的方法。然而，大多数研究忽视了根据患者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领域知识启发的网络（DKINet），将领域知识与患者的可观察临床表现相结合，这是首个面向用药推荐的动态领域知识启发的框架。具体而言，我们首先设计了一个知识驱动编码器来捕获领域信息，然后开发了一个数据驱动编码器，将领域知识整合到可观察的EHR中。为使模型具备时序决策能力，我们设计了一个显式用药编码器来学习患者的纵向依赖性。在三个公开数据集上的大量实验验证了我们方法的优越性。代码将在论文被接收后公开。