Building an intelligent diabetes Q&A system with knowledge graphs and large language models

Introduction: This paper introduces an intelligent question-answering system designed to deliver personalized medical information to diabetic patients. By integrating large language models with knowledge graphs, the system aims to provide more accurate and contextually relevant medical guidance, addressing the limitations of traditional healthcare systems in handling complex medical queries. Methods: The system combines a Neo4j-based knowledge graph with the Baichuan2-13B and Qwen2.5-7B models. To enhance performance, Low-Rank Adaptation (LoRA) and prompt-based learning techniques are applied. These methods improve the system's semantic understanding and ability to generate high-quality responses. The system's performance is evaluated using entity recognition and intent classification tasks. Results: The system achieves 85.91% precision in entity recognition and 88.55% precision in intent classification. The integration of a structured knowledge graph significantly improves the system's accuracy and clinical relevance, enhancing its ability to provide personalized medical responses for diabetes management. Discussion: This study demonstrates the effectiveness of integrating large language models with structured knowledge graphs to improve medical question-answering systems. The proposed approach offers a promising framework for advancing diabetes management and other healthcare applications, providing a solid foundation for future personalized healthcare interventions.