Leveraging Multi-Agent Systems and Large Language Models for Diabetes Knowledge Graphs

This paper presents a novel framework for constructing a diabetes-specific knowledge graph (KG) using a streamlined multi-agent system powered by Gemini-based Large Language Models (LLMs). Leveraging insights from the 2016 National Diabetes Survey (NNDS) conducted by the National Diabetes Education Program (NDEP), the framework extracts critical variables related to diagnosis, risk perception, medical advice, and self-management practices across diverse U.S. populations. By processing data from the NNDS’s extensive 94-question survey, the methodology performs adaptive ontology mapping using APIs for six major medical standards (e.g., SNOMED CT, ICD-11), ensuring semantic interoperability. Relationships between variables are identified and structured using RDF, RDFS, and OWL standards. The integration of LLMs with ontology tools like Protégé enhances automation and scalability. Results demonstrate the framework’s effectiveness in generating contextually rich and clinically relevant knowledge graphs, providing a robust foundation for advancing healthcare informatics and personalized diabetes management.