GR-MED: A MULTI-RELATIONAL GRAPH AND SEQUENCE MODELING APPROACH FOR BREAST CANCER CHEMOTHERAPY MEDICINE RECOMMENDATION WITHOUT SIDE EFFECT

Personalized medicine recommendation is a key job in contemporary healthcare, tasked with making precise and safe prescriptions based on an individual patient's profile. Most traditional recommendation models fail to accurately represent the complicated relationships between patients, diseases, and drugs and the temporal patterns of patient health records. In this work, we introduce GR-Med, a new hybrid system combining Graph Neural Networks (GNNs) and Recurrent Neural Networks (RNNs) for improving the performance of medicine recommendations. The GNN module best captures structural relationships between medical entities through utilization of heterogeneous graph data, while the RNN module captures temporal patterns in data to incorporate temporal patterns in diagnoses and treatments. We test GR-Med on actual real-world electronic health record (EHR) datasets, showing its better performance compared to current baseline models regarding recommendation accuracy, reliability, and clinical sensitivity. Our findings point to the promise of leveraging graph and sequential learning to improve smart healthcare systems and enable more intelligent clinical decision-making. This system recommends the best medicine for breast cancer chemotherapy with minimal side effects or no side effects.