中国中医科学院,北京市东城区东直门内南小街16号,100700
li_haiyan01@126.com
收稿:2025-08-30,
修回:2026-02-05,
录用:2025-11-20,
纸质出版:2026-04-17
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张艺然,李海燕,聂莹.基于大语言模型的中医黄疸病古籍语料库的构建[J].中医杂志,2026,67(08):896-903.
ZHANG Yiran,LI Haiyan,NIE Ying.Construction of a Traditional Chinese Medicine Classics Corpus for Jaundice Based on Large Language Models[J].Journal of Traditional Chinese Medicine,2026,67(08):896-903.
张艺然,李海燕,聂莹.基于大语言模型的中医黄疸病古籍语料库的构建[J].中医杂志,2026,67(08):896-903. DOI: 10.13288/j.11-2166/r.2026.08.015.
ZHANG Yiran,LI Haiyan,NIE Ying.Construction of a Traditional Chinese Medicine Classics Corpus for Jaundice Based on Large Language Models[J].Journal of Traditional Chinese Medicine,2026,67(08):896-903. DOI: 10.13288/j.11-2166/r.2026.08.015.
目的
2
基于大语言模型构建结构化中医黄疸病古籍语料库,为现代黄疸病临床与科研提供数据支撑,也为中医古籍知识的深度挖掘与利用提供思路。
方法
2
系统整合互联网开源数字化中医古籍资源,采用文献计量学方法构建黄疸病名术语库,利用Python正则表达式进行关键词匹配和语料抽取,设计“病-证-症-治-效”五元信息抽取模型,制定标注指南并采用双人背对背标注结合第三人仲裁形成标准测试集,为筛选适配实体信息抽取的最优模型,采用DeepSeek-R1与ChatGPT-o3 2个大语言模型进行对比性能评估,通过调用大模型应用程序编程接口(API)进行实体信息抽取。
结果
2
构建了涵盖从先秦至清代561本古籍、10 243条语料的中医黄疸病古籍语料库。模型对比评估结果显示,DeepSeek-R1在17类实体识别的性能全面优于ChatGPT-o3,并基于该优选模型从语料库中抽取出41 407个实体,涵盖病名(6238个)、证型(1622个)、方剂(2631个)、中药(2706个)等17个实体类型。
结论
2
基于DeepSeek-R1大语言模型构建的语料库覆盖了黄疸病核心辨证论治要素,可为后续中医黄疸病历史演变和辨证论治规律研究奠定基础,并为AI驱动的中医古籍知识挖掘提供可复制的技术范式。
Objective
2
To construct a structured corpus of traditional Chinese medicine (TCM) classics on jaundice, providing data support for modern clinical practice and scientific research on jaundice, and facilitating the deep mining and utilization of knowledge from traditional Chinese medicine (TCM) classics.
Methods
2
Open-source digitized resources of TCM classics available on the internet were systematically integrated. Bibliometric methods were employed to construct a terminology database for jaundice. Python regular expressions were used for keyword matching and corpus extraction. A five-element information extraction model of "disease-syndrome-symptom-treatment-efficacy" was designed. Annotation guidelines were developed, and a standard test set was formed through back-to-back annotation by two annotators followed by arbitration through a third party. To select the optimal model for entity information extraction, two large language models, DeepSeek-R1 and ChatGPT-o3, were chosen for comparative performance evaluation by invoking their application programming interfaces (APIs) for entity extraction.
Results
2
A TCM classics corpus for jaundice was constructed, encompassing 561 ancient books from the pre-Qin period to the Qing Dynasty, and 10
243 pieces of text. The comparative evaluation results showed that DeepSeek-R1 outperformed ChatGPT-o3 in the performance of entity recognition across all 17 entity types. Based on this optimized model DeepSeek-R1, 41
407 entities were extracted from the corpus, covering 17 entity types, including disease names (6238), syndrome types (1622), formulas (2631), and Chinese medicinal herbs (2706).
Conclusion
2
The corpus constructed using DeepSeek-R1 large language model covers the essential syndrome differentiation and treatment elements for jaundice, laying a solid foundation for subsequent research on the historical evolution and syndrome differentiation and treatment rules of jaundice in TCM. It also provides a replicable technical paradigm for AI-driven knowledge mining from TCM classics.
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