  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.
