HI-DR: Exploiting Health Status-Aware Attention and an EHR Graph plus for Effective Medication Recommendation

Abstract

We focus on the medication recommendation problem aiming to recommend accurate medications for a patient's current visit. Most existing methods for this problem utilize the patient's current health status, medications prescribed at her past visits, and an Electronic Health Records (EHR) graph which represents whether medications have been co-prescribed. However, we point out their two key limitations: (1) they have difficulty in utilizing only the medications which have been prescribed in health status similar to the patient's current health status, regardless of whether they are prescribed at her past visits or at other patients' visits; (2) for two medications that have ever been co-prescribed, their EHR graph does not consider the degree to which one medication is prescribed together when the other is prescribed. To address these two limitations, we propose a novel medication recommendation framework, named HI-DR (pronounced as 'Hi Doctor'), composed of following two core ideas: (Idea 1) Health status-aware attentIon; (Idea 2) an electronic health recorDs gRaph+. Extensive experiments on real-world datasets demonstrate the significant superiority of HI-DR (up to 18.69% higher accuracy than the best competitor) and the effectiveness of two core ideas in HI-DR. Code - https://github.com/Bigdasgit/HI-DR