Knowledge-Induced Medicine Prescribing Network for Medication Recommendation

TL;DR

This paper introduces KindMed, a novel knowledge-induced network that enhances medication recommendation by integrating external medical knowledge graphs and temporal clinical data for personalized treatment suggestions.

Contribution

The study proposes a new framework that combines knowledge graphs with relation-aware graph learning and hierarchical sequence modeling for improved medication recommendation.

Findings

Achieved superior recommendation accuracy over baseline models.

Effectively integrated external medical knowledge into clinical decision-making.

Demonstrated robustness on real-world EHR datasets.

Abstract

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.