Exploration of Chronic Disease Pre-triage System Based on LLM and RGA

Aiming at the problems that early screening of chronic diseases relies too much on doctors’ experience, the utilization of patients’ health data is not comprehensive, and the traditional deep learning model lacks clinical interpretability and traceability, this paper explores the application of large language model and retrieval enhancement generation technology in the classification of chronic diseases. Time alignment and semantic links are performed on multimodal data such as patient lifestyle, health records, electronic medical records, laboratory indicators, and medical images through data preprocessing. Based on clinical guidelines and medical literature, a domain vector knowledge base is constructed, and prompt word engineering and RAG technology are integrated to dynamically retrieve authoritative medical literature, providing context for large language model reasoning, realizing collaborative optimization of multimodal data fusion and dynamic knowledge incremental updating, and breaking through the limitations of traditional large models resulted from poor domain data quality and static knowledge storage. The experiments show that the accuracy of chronic diseases classification can be improved to more than 91% by fine-tuning the large language model and RAG technology, which provides a technical support for intelligent pre-triage of chronic diseases.