  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.
The code is available at https://github.com/jmyissb/HealthLLM.
