Abstract: Addressing critical gaps in existing research regarding dynamic prescription adaptation to evolving patient conditions and drug-drug interactions,this study proposes MRNET(Medical Recommendation Network),a novel dynamic disease modeling framework for optimized drug combination recommendation.The model uses the graph convolutional network for pre-training to mine the potential association information between entities by associating related entities and providing data support for subsequent dynamic disease modeling and drug combination recommendation.Subsequectly,the MRNET employs Transformer-based temporal modeling to capture longitudinal disease progression patterns,effectively characterizing dynamic clinical state transitions.At the same time,by comparing the similarity of diagnoses and procedures horizontally,the applicability and differences of different prescriptions under similar conditions and diagnoses can be considered.The combination of horizontal comparison and longitudinal disease dynamics enables the model to more comprehensively evaluate the rationality and applicability of drug combinations in the drug recommendation process.Finally,the introduction of drug side effects can screen out safer and more effective drug combinations,and improve the accuracy and safety of drug recommendations.Experimental validation demonstrates MRNET’s superior performance,achieving improvements of 2.07%,1.96%,and 1.72% in Jaccard similarity,F1-score,and PRAUC metrics respectively over state-of-the-art baselines.The advantages of MRNET in these important metrics fully demonstrate its superiority in drug combination recommendation.

Key words: Drug combination recommendations, Dynamic disease modeling, Graph convolutional network, Transformer, Medication side effects