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Application of ChatGPT in Symptom Information Preprocessing of Electronic Medical Record of Rheumatology
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Jinlong YU1, Lei ZHANG2, Ning XU2, Lifeng FA2, Kuo YANG3, Zhenzhen HAN4, Hongtao GUO4
Chinese Archives of Traditional Chinese Medicine | 2025, 43(3) : 24 - 29
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Chinese Archives of Traditional Chinese Medicine | 2025, 43(3): 24-29
• Digital Traditional Chinese Medicine •
Application of ChatGPT in Symptom Information Preprocessing of Electronic Medical Record of Rheumatology
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Jinlong YU1, Lei ZHANG2, Ning XU2, Lifeng FA2, Kuo YANG3, Zhenzhen HAN4, Hongtao GUO4
Affiliations
  • 1.Institute of Information on Traditional Chinese Medicine，China Academy of Chinese Medical Sciences，Beijing 100700，China
  • 2.Traditional Chinese Medicine Data Center of China Academy of Chinese Medical Sciences，Beijing 100700，China
  • 3.School of Computer and Information Technology，Beijing Jiaotong University，Beijing 100081，China
  • 4.The First Affiliated Hospital of Henan University of Chinese Medicine，Zhengzhou 450000，Henan，China
Published: 2025-03-10 doi: 10.13193/j.issn.1673-7717.2025.03.005
Outline
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Objective

Information extraction is an important means of natural language processing. Based on the natural language processing ability of ChatGPT，this paper extracted the symptom information from traditional Chinese medicine electronic medical records in rheumatism department through ChatGPT.

Methods

Through the small sample learning based on ChatGPT large model，the information extraction and standardization of chief complaint and present medical history，specialist examination and tongue and pulse data in rheumatic electronic medical records were realized. At the same time，a large model ChatGLM was selected for the same small sample learning as ChatGPT，the same information extraction task was completed and the accuracy of the extraction results of the two large models was compared.

Results

Aiming at the problem of medical named entity recognition，the accuracy of ChatGPT after small sample learning reached 98.7% in chief complaint symptom extraction task，92% in specialist examination and extraction task，and 98% in tongue and pulse image extraction and standardization task. The correct rates of ChatGLM on the same three tasks were 85.7%，91% and 98% respectively. From the extraction results，the two large models had different defects in the extraction task，but on the whole，ChatGPT performed better.

Conclusion

This paper realized the TCM symptom information preprocessing based on ChatGPT small sample learning，which was more convenient and efficient than the traditional model，and performed better than ChatGLM as a whole，which provided a new idea for TCM clinical text information extraction research.

ChatGPT  /  TCM electronic medical records  /  symptoms  /  data preprocessing  /  information extraction
喻金龙, 张磊, 许宁, 法立峰, 杨扩, 韩真真, 郭洪涛. ChatGPT在风湿科中医电子病历症状信息预处理中的应用. 中华中医药学刊, 2025 , 43 (3) : 24 -29 . DOI: 10.13193/j.issn.1673-7717.2025.03.005
Jinlong YU, Lei ZHANG, Ning XU, Lifeng FA, Kuo YANG, Zhenzhen HAN, Hongtao GUO. Application of ChatGPT in Symptom Information Preprocessing of Electronic Medical Record of Rheumatology[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (3) : 24 -29 . DOI: 10.13193/j.issn.1673-7717.2025.03.005
Appendix
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Year 2025 volume 43 Issue 3
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Article Info
doi: 10.13193/j.issn.1673-7717.2025.03.005
  • Online Date：2026-10-30
  • Published：2025-03-10
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Affiliations
    1.Institute of Information on Traditional Chinese Medicine，China Academy of Chinese Medical Sciences，Beijing 100700，China
    2.Traditional Chinese Medicine Data Center of China Academy of Chinese Medical Sciences，Beijing 100700，China
    3.School of Computer and Information Technology，Beijing Jiaotong University，Beijing 100081，China
    4.The First Affiliated Hospital of Henan University of Chinese Medicine，Zhengzhou 450000，Henan，China
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https://castjournals.cast.org.cn/joweb/zhzyyxk/EN/10.13193/j.issn.1673-7717.2025.03.005
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