Construction of HIV Prevention and Treatment System by Integrating Large Language Model with Knowledge Graph-RAG

Corresponding author: zhao jian, 52360328@qq.com

DOI: 10.12201/bmr.202511.00061

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  Abstract: Purpose/Significance To construct a knowledge graph for HIV prevention and treatment, achieve intelligent knowledge Q&A, provide a scientific basis for HIV prevention and treatment, and reduce the burden of AIDS. Method/Process Systematically sort out multi-source heterogeneous information such as expert consensus and diagnosis and treatment guidelines on HIV prevention and treatment at home and abroad, and extract entities and the relationships between entities in combination with Prompt and NLP big data technology; Combining knowledge graph (KG), retrieval-augmented generation (RAG) and the natural language processing capabilities of LLM, a question-answering system suitable for the field of HIV is constructed. Result/Conclusion The system based on LLM and KG-RAG mechanisms in this study can enhance the accuracy and effectiveness of HIV knowledge Q&A, providing a feasible path for the intelligent development of HIV prevention and treatment.

  Key words: HIV prevention and treatment; Knowledge graph; Retrieval-Augmented Generation; Large language model; Intelligent question answering

  Submit time: 20 November 2025

  Copyright: The copyright holder for this preprint is the author/funder, who has granted biomedRxiv a license to display the preprint in perpetuity.
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