  Extensive adoption of electronic health records (EHRs) offers opportunities
for their use in various downstream clinical analyses. To accomplish this
purpose, enriching an EHR cohort with external knowledge (e.g., standardized
medical ontology and wealthy semantics) could help us reveal more comprehensive
insights via a spectrum of informative relations among medical codes.
Nevertheless, harnessing those beneficial interconnections was scarcely
exercised, especially in the medication recommendation task. This study
proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed) to
recommend medicines by inducing knowledge from myriad medical-related external
sources upon the EHR cohort and rendering interconnected medical codes as
medical knowledge graphs (KGs). On top of relation-aware graph representation
learning to obtain an adequate embedding over such KGs, we leverage
hierarchical sequence learning to discover and fuse temporal dynamics of
clinical (i.e., diagnosis and procedures) and medicine streams across patients'
historical admissions to foster personalized recommendations. Eventually, we
employ attentive prescribing that accounts for three essential patient
representations, i.e., a summary of joint historical medical records, clinical
progression, and the current clinical state of patients. We validated the
effectiveness of our KindMed on the augmented real-world EHR cohorts, achieving
improved recommendation performances against a handful of graph-driven
baselines.
