MGRN: Towards robust drug recommendation via multi-view gating retrieval network

Abstract Motivation Drug recommendation aims to allocate safe and effective drug combinations based on the patient’s health status from electronic health records (EHRs), which is crucial to assist clinical physicians in making decisions. However, the existing drug recommendation works face two key challenges: (i) difficulty in fully representing the patient’s health status leads to biased drug representation; (ii) only focusing on diagnostic representations of multiple visits, neglecting the modeling of patient drug history. Results To address the above limitations, we propose a Multi-view Gating Retrieval Network (MGRN) for robust drug recommendation. We design visit-, sequence-, and token-level views to provide different perspectives on the interaction between patient and drugs, obtaining a more comprehensive representation of drugs. Moreover, we develop a gating drug retrieval module to capture critical drug information from multiple visits, which can assist in recommending more reasonable drug combinations for the current visit. When evaluated on publicly real-world MIMIC-III and MIMIC-IV datasets, the proposed MGRN establishes a new benchmark performance, particularly achieving improvements of 1.36%, 1.71%, 1.21% and 2.12%, 2.36%, 1.81% in Jaccard, PRAUC and F1-score, respectively, compared to state-of-the-art (SOTA) models. Availability and implementation The code is available at: Https://github.com/kyosen258/MGRN.git. Supplementary data Supplementary data are available at Bioinformatics online.