Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
This addresses personalized disease prediction for healthcare applications, but it appears incremental as it builds on existing LLM and RAG methods.
The paper tackled the problem of integrating healthcare AI with individual patient needs by proposing Health-LLM, a personalized retrieval-augmented disease prediction system that combines large-scale feature extraction and medical knowledge scoring, and results show it surpasses existing systems in effectiveness.
Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment. However, there are conspicuous challenges such as vast data volumes and inconsistent symptom characterization standards, preventing full integration of healthcare AI systems with individual patients' needs. To promote professional and personalized healthcare, we propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. 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 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 accuracy of disease prediction. We experiment on a large number of health reports to assess the effectiveness of Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to significantly advance disease prediction and personalized health management.