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Research on Automated Construction and Clinical Decision Support of Traditional Chinese Medicine Gynecology Knowledge Graphs Based on ChatGLM
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Shaoliang TANG1, 2, Qiuyu LONG1, 2, Junyan LI1, 2, Junlong SHEN2, Nan ZHAO3
Chinese Archives of Traditional Chinese Medicine | 2026, 44(1) : 1 - 6
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Chinese Archives of Traditional Chinese Medicine | 2026, 44(1): 1-6
• New Opinion of Ph.D. Supervisor •
Research on Automated Construction and Clinical Decision Support of Traditional Chinese Medicine Gynecology Knowledge Graphs Based on ChatGLM
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Shaoliang TANG1, 2, Qiuyu LONG1, 2, Junyan LI1, 2, Junlong SHEN2, Nan ZHAO3
Affiliations
  • 1.Department of Big Data Management and Application, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China
  • 2.School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China
  • 3.School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China
Published: 2026-01-10 doi: 10.13193/j.issn.1673-7717.2026.01.001
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The study aims to construct a knowledge graph for traditional Chinese medicine（TCM）gynecology based on large language models. The goal is to systematically organize and present the complex knowledge system of this field，revealing the intrinsic connections and evolution patterns among diseases，thereby enhancing the precision of clinical decision-making. The study selected 349 TCM gynecology papers from China National Knowledge Infrastructure（CNKI）. ChatGLM was used with multi-round prompt templates to achieve zero-shot entity relationship extraction. Evaluation metrics were designed to objectively assess the extraction results. Efficient processing was achieved through API interfaces interacting with the model. The Neo4j graph database was used to construct and visualize the knowledge graph. The study shows that ChatGLM performs better in knowledge extraction compared to traditional Bi-LSTM-CRF models，significantly improving precision，recall and F1 scores. The constructed knowledge graph effectively supports the clinical decision-making in TCM gynecology，enhancing the diagnostic efficiency and accuracy and demonstrating the practical value.

large language model  /  TCM gynecology  /  knowledge graph  /  knowledge extraction  /  Neo4j  /  Bi-LSTM-CRF
汤少梁, 龙秋予, 李君妍, 申俊龙, 赵楠. 基于ChatGLM的中医妇科知识图谱自动化构建与临床决策支持研究. 中华中医药学刊, 2026 , 44 (1) : 1 -6 . DOI: 10.13193/j.issn.1673-7717.2026.01.001
Shaoliang TANG, Qiuyu LONG, Junyan LI, Junlong SHEN, Nan ZHAO. Research on Automated Construction and Clinical Decision Support of Traditional Chinese Medicine Gynecology Knowledge Graphs Based on ChatGLM[J]. Chinese Archives of Traditional Chinese Medicine, 2026 , 44 (1) : 1 -6 . DOI: 10.13193/j.issn.1673-7717.2026.01.001
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Year 2026 volume 44 Issue 1
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doi: 10.13193/j.issn.1673-7717.2026.01.001
  • Online Date：2026-10-14
  • Published：2026-01-10
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Affiliations
    1.Department of Big Data Management and Application, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China
    2.School of Health Economics and Management, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China
    3.School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, Jiangsu, China
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表12种不同金属材料的力学参数
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小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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