BH ^<b>3</b> -MedRec: Bilateral Hierarchical Heterogeneous Hypergraph Convolution Network for Medication Recommendation

The development of artificial intelligence and medical informatics has empowered the medication recommendation systems with enhanced capabilities. However, existing methods struggle with the data imbalance problem in Electronic Health Records (EHRs), where the majority of records are concentrated on a limited subset of common diagnoses, procedures, and medications. It hampers the models’ ability to recommend appropriate medications when dealing with uncommon or multifaceted cases. In addition, existing approaches often fail to adequately model the complex relationships inherent in heterogeneous medical data sources, especially medication molecular structure information. This gap restricts the potential for uncovering meaningful associations among diverse clinical entities. To address these issues, we design a hierarchical attention-based pretraining strategy, leveraging the semantic hierarchies of medical entity codes to facilitate knowledge transfer, so as to alleviate the challenge of data imbalance. Furthermore, we design a novel bilateral hierarchical heterogeneous hypergraph convolution network for medication recommendation. Specifically, we construct specialized hypergraphs for both EHR data and medication molecular structure data, enabling hypergraph convolution to capture high-order relationships while promoting bilateral knowledge enhancement between these heterogeneous data sources. This comprehensive integration allows the model to effectively capture the relationships among clinical and molecular information. Experimental results on different hospital departments of MIMIC-III and MIMIC-IV datasets demonstrate the superior performance of our model compared to state-of-the-art methods. Our source code is released at: https://github.com/LusiaZ/BH3-MedRec .