MedAlign: Enhancing Combinatorial Medication Recommendation with Multi-modality Alignment

Combinatorial Medication Recommendation (CMR) based on multimodal Electronic Health Records (EHRs) is a promising yet challenging frontier in AI-driven healthcare. Existing approaches usually rely on feature extraction from individual modalities without explicitly aligning information across different data sources. As a result, they may ignore complementary information from other modalities, leading to suboptimal representations for CMR. To this end, we propose MedAlign, a novel combinatorial Medication recommendation framework with multi-modality Alignment. Specifically, we first design a distribution-aware multimodal medication alignment module. This aligns distinct modality distributions of medications within a unified latent space, generating consistent medication representations. Furthermore, we introduce a longitudinal multi-view patient aggregation module, which aggregates the historical visits of patients with multi-view information to form informative patient representations. Finally, we propose a combinatorial medication recommendation module, enabling an accurate and safe medication recommendation combination for each patient. Extensive experiments on two real-world multimodal EHR datasets demonstrate the effectiveness of our MedAlign.