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Knowledge-Induced Medicine Prescribing Network for Medication Recommendation

arXiv 2310.14552 ↗First posted 23 Oct 2023cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 13 Oct 2026). Builds on 17 of its 46 references that are in the map. First posted on arXiv 23 Oct 2023.

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References · 17

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5 more
  1. 2018GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combinationcited by 80
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  3. 2021SafeDrug: Dual Molecular Graph Encoders for Recommending Effective and Safe Drug Combinationscited by 51
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