Health-LLM: Personalized Retrieval-Augmented Disease Prediction System: Health-LLM

Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potential in intelligent medical treatment. To promote professional and personalized healthcare, we propose an innovative framework, \textbf{Health-LLM}, which combines large-scale feature extraction and trade-off scoring of medical knowledge. Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to the Large Language Model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve the accuracy of disease prediction. We experimented with a large number of health reports to assess the effectiveness of the Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to advance disease prediction and personalized health management significantly.