A Collaborative Cross-Attention Drug Recommendation Model Based on Patient and Medical Relationship Representations

The purpose of drug recommendation is to predict the effective and safe drug combinations required for the current visit based on the historical medical data of patients. How to better mine the hidden relationship in the medical data and effectively improve the accuracy of drug recommendation are research hotspots in the medical field. This paper proposes a Collaborative Cross-attention Drug Recommendation model (CCDR) based on patient and medical relationship representations, which mines medical data from two aspects to enhance the representation ability of the model. CCDR obtains patient representation vectors by modeling the patients’ historical sequence data using Bidirectional Gated Recurrent Unit. Meanwhile, CCDR designs a medical graph structure data learning method based on relationship division to better capture the complex association relationships among diagnoses, procedures, and drugs. Finally, the representation capability of the model is enhanced by introducing a collaborative cross-attention mechanism to fuse the information obtained from both medical sequence and graph structure data. The experimental results show that the CCDR model can effectively improve the performance of drug recommendation.