EDRMM: enhancing drug recommendation via multi-granularity and multi-attribute representation.
Journal:
BMC bioinformatics
Published Date:
Jul 10, 2025
Abstract
BACKGROUND: Drug recommendation is a crucial application of artificial intelligence in medical practice. Although many models have been proposed to solve this task, two challenges remain unresolved: (i) most existing models use all historical visits as input, overlooking fine-grained correlations between historical and current information; (ii) Electronic Health Records (EHRs) are underutilized, with only partial information considered to describe patient conditions. To tackle the challenges, we propose a novel drug recommendation model, denoted by EDRMM, which incorporates multi-granularity and multi-attribute information into representation learning. We develop a longitudinal attribute-level history selection mechanism to effectively identify fine-grained historical information that is highly relevant to a patient's current clinical conditions. We analyze the impact of key Electronic Health Record (EHR) attributes, demonstrating that incorporating such attributes into patient representations can further boost performance. We also design an adaptive global Drug-Drug Interaction (DDI) risk regularization term for the DDI loss function to better balance accuracy and safety during training.
