KindMed: Knowledge-Induced Medicine Prescribing Network for Medication
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Ahmad Wisnu Mulyadi Heung-Il Suk
Extensive adoption of electronic health records (EHRs) offers opportunities
for its use in various clinical analyses. We could acquire more comprehensive
insights by enriching an EHR cohort with external knowledge (e.g., standardized
medical ontology and wealthy semantics curated on the web) as it divulges a
spectrum of informative relations between observed medical codes. This paper
proposes a novel Knowledge-Induced Medicine Prescribing Network (KindMed)
framework to recommend medicines by inducing knowledge from myriad
medical-related external sources upon the EHR cohort, rendering them as medical
knowledge graphs (KGs). On top of relation-aware graph representation learning
to unravel an adequate embedding of such KGs, we leverage hierarchical sequence
learning to discover and fuse clinical and medicine temporal dynamics across
patients' historical admissions for encouraging personalized recommendations.
In predicting safe, precise, and personalized medicines, we devise an attentive
prescribing that accounts for and associates three essential aspects, i.e., a
summary of joint historical medical records, clinical condition progression,
and the current clinical state of patients. We exhibited the effectiveness of
our KindMed on the augmented real-world EHR cohorts, etching leading
performances against graph-driven competing baselines.