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Development of an intelligent question-answering model and its application in public health: based on RAG technology
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Jie LIU, Shuxin HAO, Bohan LIU, Yue LIU
Chinese Journal of Public Health | 2026, 42(5) : 556 - 562
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Chinese Journal of Public Health | 2026, 42(5): 556-562
• Special Report—Applications of Artificial Intelligence in Public Health •
Development of an intelligent question-answering model and its application in public health: based on RAG technology
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Jie LIU, Shuxin HAO, Bohan LIU, Yue LIU
Affiliations
  • China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China
Published: 2026-05-10 doi: 10.11847/zgggws1148202
Outline
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Objective

To develop an intelligent question-answering model based on retrieval-augmented generation (RAG) to assist public health professionals in efficiently and accurately extracting key information from technical documents such as regulations, guidelines, manuals, and literature, thereby improving knowledge acquisition efficiency and standardized application capabilities.

Methods

On the basis of RAG, this study integrates an automated text preprocessing mechanism and employs the LangChain framework to construct a context-aware unified retrieval-augmented question-answering model powered by large language models (CURA-LLM). This model combines a threshold-managed information retrieval module with a response generation module supported by historical dialogue records.

Results

CURA-LLM demonstrated outstanding performance on three representative public health datasets related to operational guidelines, testing procedures, and legal regulations, with the F1 scores of 0.941, 0.891, and 0.947, and the cosine similarities of 0.968, 0.929, and 0.963, respectively. Moreover, CURA-LLM outperformed three existing domain-specific question-answering models in terms of retrieval relevance and answer accuracy.

Conclusions

CURA-LLM provides effective support for the intelligent interpretation and rapid extraction of knowledge from public health technical documents. It can assist personnel in quickly responding and standardizing execution in scenarios such as emergency responses, routine management, and professional training, offering strong potential for practical application.

retrieval-augmented generation  /  large language model  /  intelligent question answering  /  public health
刘婕, 郝舒欣, 刘博涵, 刘悦. 智能问答模型构建以及在公共卫生领域应用:基于检索增强生成技术. 中国公共卫生, 2026 , 42 (5) : 556 -562 . DOI: 10.11847/zgggws1148202
Jie LIU, Shuxin HAO, Bohan LIU, Yue LIU. Development of an intelligent question-answering model and its application in public health: based on RAG technology[J]. Chinese Journal of Public Health, 2026 , 42 (5) : 556 -562 . DOI: 10.11847/zgggws1148202
Year 2026 volume 42 Issue 5
83
33
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Article Info
doi: 10.11847/zgggws1148202
  • Receive Date:2025-06-26
  • Online Date:2026-10-23
  • Published:2026-05-10
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History
  • Received:2025-06-26
  • Revised:2025-08-11
  • Accepted:2025-09-30
Affiliations
    China CDC Key Laboratory of Environment and Population Health, National Institute of Environmental Health, Chinese Center for Disease Control and Prevention, Beijing 100021, China

Corresponding:

LIU Yue, E-mail:
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