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Health-LLM: Personalized Retrieval-Augmented Disease Prediction System
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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.
Forward citations
Cited by 3 Pith papers
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Retrieval-Augmented Interpretable Learning: Towards Task-Specific Zero-Shot Models in Healthcare
RAIL retrieves past task-specific linear models by semantic similarity and synthesizes a zero-shot interpretable predictor in the original feature space, reaching 73.4% accuracy on held-out clinical procedure tasks.
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HealthBranches: Synthesizing Clinically-Grounded Question Answering Datasets via Decision Pathways
A semi-automated pipeline turns clinical decision trees into 4,063 medical Q&A pairs with explicit reasoning paths, and early LLM benchmarks show models improve when given those paths.
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Dr. GPT Will See You Now, but Should It? Exploring the Benefits and Harms of Large Language Models in Medical Diagnosis using Crowdsourced Clinical Cases
In a physician-rated crowdsourced study, 76% of LLM responses to everyday health queries were valid, with GPT-4o highest (85%) and Llama3-8b lowest (50%); RAG did not consistently improve responses.
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