融合查询意图与大语言模型的医学信息检索方法研究

doi:10.3772/j.issn.1000-0135.2026.08.006

情报学报

2026, Vol. 45

Issue (8): 1154-1165 DOI: 10.3772/j.issn.1000-0135.2026.08.006

情报技术与应用

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融合查询意图与大语言模型的医学信息检索方法研究

韩普^1,2, 刘森嶺¹, 李雄¹, 王伟¹

1.南京邮电大学管理学院，南京 210003
2.数据工程与知识服务省高校重点实验室（南京大学），南京 210023

Research on Medical Information Retrieval Methods Integrating Query Intent and Large Language Models

Han Pu^1,2, Liu Senling¹, Li Xiong¹, Wang Wei¹

1.School of Management, Nanjing University of Posts and Telecommunications, Nanjing 210003
2.Key Laboratory of Data Engineering and Knowledge Services in Provincial Universities (Nanjing University), Nanjing 210023

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摘要医学信息检索任务旨在从多样化的医疗数据源中获取与用户查询高度相关的信息。传统信息检索方法主要依赖关键词匹配或表层语义相似度建模，难以有效弥合口语化查询与规范医学知识表达之间的语义鸿沟。为此，本文提出一种融合查询意图与大语言模型的医学信息检索方法。首先，为增强模型对医学文本的表示能力，本文基于中文医疗查询意图识别主流数据集为Chinese-RoBERTa-wwm-ext模型注入任务知识，以准确识别用户查询意图。其次，利用意图识别结果引导大语言模型对原始查询进行语义优化与重构，以提升大语言模型查询优化的准确性和稳定性；为避免原始语义损失并增强语义信息，分别对原始查询和优化查询进行编码与检索，并通过相似度得分加权融合获取top-k候选文档。最后，将原始查询与优化查询组合形成联合表征，输入跨编码器与候选文档进行重排序，得到最终的检索结果。在公开数据集CmedqaRetrieval和DXYDiseaseRetrieval上的实验结果表明，本文方法能够显著提升医学信息检索的准确性与相关性，并在四个大语言模型上表现出良好的泛化性与稳健性。

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	韩普
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关键词 ：医学信息检索, 大语言模型, 意图识别, 查询优化, 两阶段检索

收稿日期: 2025-11-11

基金资助:江苏省高校哲学社会科学创新团队（数智驱动的健康大数据管理创新团队）项目；江苏高校青蓝工程中青年学术带头人项目。

作者简介: 韩普，1983年生，博士，教授，博士生导师，主要研究方向为智能信息处理、医疗健康数据分析；刘森嶺，2002年生，硕士，主要研究方向为数据分析与处理；李雄，2001年生，硕士，主要研究方向为数据分析与处理；王伟，通信作者，1994年生，博士，讲师，主要研究方向为智能信息处理，E?mail：20220181@njupt.edu.cn；

引用本文:

韩普, 刘森嶺, 李雄, 王伟. 融合查询意图与大语言模型的医学信息检索方法研究[J]. 情报学报, 2026, 45(8): 1154-1165.
Han Pu, Liu Senling, Li Xiong, Wang Wei. Research on Medical Information Retrieval Methods Integrating Query Intent and Large Language Models. 情报学报, 2026, 45(8): 1154-1165.

链接本文:

https://qbxb.istic.ac.cn/CN/10.3772/j.issn.1000-0135.2026.08.006 或 https://qbxb.istic.ac.cn/CN/Y2026/V45/I8/1154

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