Semantic ID Learning for Safe Medication Recommendation

Medication recommendation from electronic health records requires jointly modeling longitudinal patient histories, unordered intra-visit clinical concepts, and drug-drug interaction (DDI) safety constraints. Existing methods either represent clinical concepts as flat vocabulary indices without fully leveraging pre-trained biomedical semantics, or enforce DDI constraints through post-hoc filtering, thereby decoupling safety from model optimization. We propose SID-MedRec, a two-stage framework that addresses these limitations through three designs. First, a residual quantized variational autoencoder (RQ-VAE) learns hierarchical discrete semantic IDs from BioBERT embeddings, providing a unified representation for diagnoses, procedures, and medications. Second, a set-aware temporal architecture combines Set Transformers for intra-visit set aggregation with GRU encoders for inter-visit sequence modeling, capturing the structural characteristics of EHR data. Third, an adaptive DDI-aware loss dynamically balances recommendation accuracy and DDI avoidance based on a predefined target DDI rate. Experiments on MIMIC-III and MIMIC-IV demonstrate that SID-MedRec achieves consistent improvements over strong baselines in recommendation performance while maintaining competitive DDI rates.