Prescription errors and adverse drug interactions remain critical challenges in healthcare, particularly in developing countries like Vietnam, where medical data is often fragmented and unstructured. This study proposes a symptom-based drug recommendation system to assist physicians and pharmacists in making accurate prescriptions. The system utilizes artificial intelligence and deep learning to analyze multidimensional medical data, including symptoms, drug side effects, comorbidities, and patient feedback. Three approaches are explored: Retrieval-Augmented Generation (RAG), Knowledge-Augmented Generation (KAG), and Graph Retrieval-Augmented Generation (GR AG). RAG integrates retrieval-based information with generative models, KAG incorporates domain knowledge for better reasoning, and GRAG leverages knowledge graphs with Graph Neural Networks (GNNs) such as Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), Message Passing Neural Networks (MPNN), Relational Graph Convolutional Networks (R-GCN), GraphSAGE, and Graph Transformers (GTs). These GNN models predict drug candidates and risk scores, which inform RAG queries for external knowledge. Evaluations on MIMIC-III (subset) and Medical Recommendation System datasets show GTs achieving 92.77% accuracy in symptom-to-disease classification, with GRAG yielding an F1-score of 0.407 in drug recommendation (19.0% improvement over RAG) and reducing DDI rate to 2.8%. This system could reduce errors, enhance personalized prescriptions, and optimize healthcare in resource-constrained settings.
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