Collaborative Relation Augmentation With Hierarchical Prescription Inference for Medication Recommendation

Medication recommendation systems have emerged as crucial tools in healthcare, offering personalized and effective drug combinations tailored to individual patient's clinical profiles. However, most existing approaches primarily focus on drug prediction by analyzing patient-drug interactions, often neglecting the intricate correlations between diseases and drugs. To address above limitation, this paper proposes a novel Collaborative Relation augmentation with Hierarchical Prescription inference network (CRHP) for effective medication recommendation. CRHP first constructs multiple covariance knowledge graphs to capture fine-grained interaction relationships between different entities from a global perspective. Based on self-built knowledge graphs, CRHP designs a collaborative relation augmented learning module, which introduces hypergraph convolutional networks to capture high-order association information between different entities. Moreover, CRHP devises a hierarchical prescription inference module that formulates drug prescriptions based on both current and historical patient information. The extensive experiments on two publicly available real-world medical datasets, MIMIC-III and MIMIC-IV, demonstrate the effectiveness of CRHP. The results indicate significant performance improvements over baseline methods, with gains of 2.12 and 1.31 in Jaccard, 1.91 and 1.83 in PRAUC, and 1.79 and 0.98 in F1-score (in percentage points).