STRMeR:Enhancing Medication Recommendation via Mining Complex Structures and Temporal Relationships from EHR

Recent studies increasingly explore the use of Electronic Health Records (EHR)-including diagnostic, surgical, and historical visit data-for medication recommendation. An effective EHR representation must integrate both the temporal relationships across visits and medical events, and their intrinsic clinical features. Existing methods fall into three categories: instance-based, sequential, and graphical representations. Instance-based approaches rely only on current visit data, ignoring patient history and leading to limited accuracy. Sequential methods focus solely on visit timelines, while graphical ones capture structural relationships among medical events but overlook temporal dynamics. To combine the strengths of both, we propose STRMeR, a novel model that enhances medication recommendation by mining complex structures and temporal patterns from EHR. It dynamically tracks a patient's health state by propagating structural information from medical events to visits and incorporating temporal visit embeddings. We also introduce an Adaptive Patch Mechanism to identify local time-related patterns in a data-driven way, and a Multi-Scale Convolution Mechanism to capture short-, medium-, and longterm temporal dependencies. Evaluated on the MIMIC-III and MIMIC-IV benchmarks, STRMeR outperforms current state-of-the-art methods.