RaceMED: A Race-Aware Approach to Accurate and Fair Medication Recommendation
Abstract
Clinical evidence indicates that (1) disease prevalence differs across racial groups and (2) medication prescriptions for identical diagnoses and procedures may vary across racial groups. However, existing medication recommendation methods have not integrated these racial properties into the design of their encoder and predictor architectures. To address this gap, we propose RaceMED, which incorporates properties (1) and (2) through a dual-branch encoder composed of race-specific and race-general branches that capture idiosyncratic features unique to each racial group and general patterns shared across all patients, and a predictor based on race-aware attention that restricts cross-patient visit references to patients from the same racial group, preventing inappropriate medication transfer across racial groups. Extensive experiments demonstrate that RaceMED consistently outperforms ten state-of-the-art competitors, achieving up to 12.24% improvement in accuracy while reducing performance disparities across racial groups by up to 31.77%, thereby improving fairness. These findings demonstrate that explicitly modeling racial properties is essential for improving both accuracy and fairness, a dimension that has been largely underexplored in the medication recommendation domain.