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Study on Construction of TCM Syndrome Differentiation Model of Cancer Pain Based on Artificial Intelligence
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Bing WU1, Liuliu ZHANG1, Yong’an GUO2, Qijie QIAN2, Jing WU3
Chinese Archives of Traditional Chinese Medicine | 2025, 43(3) : 19 - 23
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Chinese Archives of Traditional Chinese Medicine | 2025, 43(3): 19-23
• Digital Traditional Chinese Medicine •
Study on Construction of TCM Syndrome Differentiation Model of Cancer Pain Based on Artificial Intelligence
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Bing WU1, Liuliu ZHANG1, Yong’an GUO2, Qijie QIAN2, Jing WU3
Affiliations
  • 1.Jiangsu Cancer Hospital,Jiangsu Institute of Cancer Research,The Affiliated Cancer Hospital of Nanjing Medical University,Nanjing 210009,Jiangsu,China
  • 2.Nanjing University of Posts and Telecommunications,Nanjing 210023,Jiangsu,China
  • 3.Jiangsu Nursing Society,Nanjing 210008,Jiangsu,China
Published: 2025-03-10 doi: 10.13193/j.issn.1673-7717.2025.03.004
Outline
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Objective

To establish the TCM syndrome differentiation model of cancer pain based on artificial intelligence,so as to provide basis for the development of TCM syndrome differentiation of cancer pain.

Methods

The TCM medical record data of cancer pain was collected from CNKI,VIPChinese Science and Technology periodical Database,Wanfang database,China Biology Medicine disc,and Ancient and Modern Medical Record Cloud Platform from the embellishment time up to September 2023. Machine learning algorithms,such as linear support vector classification(LSVC),nearest centroid(NC),multinomial Na?ve Bayes(MNB),random forest(RF),stochastic gradient descent(SGD)and multilayer perceptron(MLP)were used to quantitatively analyze the data set,and the syndrome differentiation model of cancer pain was established. The model was evaluated by 5-fold cross validation. The evaluation indexes included Accuracy,Precision,Recall,F1 score and AUC value.

Results

There were 72 input variables and 6 output variables for TCM syndromes of cancer pain. The Accuracy,Precision,Recall and F1 score of the six models were all above 0.89,and the AUCvalue was above 0.94. Among them,the Accuracy,Precision and Recall of the nearest centroid model were the highest. The Accuracy,precision,recall and F1 score were 0.978,0.980,0.978 and 0.977,respectively,and the AUCvalue was 0.986,which was higher than other algorithm models. The TCM interpretation of the parameters basically accorded with the principle of TCM diagnosis.

Conclusion

The TCM syndrome differentiation model of cancer pain based on the nearest centroid algorithm model has high diagnosis and prediction ability,which can provide reference for clinical workers to carry out TCM intelligent syndrome differentiation of cancer pain.

artificial intelligence  /  machine learning  /  cancer pain  /  TCM syndrome differentiation model  /  medical records  /  syndrome differentiation
吴冰, 张柳柳, 郭永安, 钱琪杰, 吴静. 基于人工智能的癌痛中医辨证模型构建研究. 中华中医药学刊, 2025 , 43 (3) : 19 -23 . DOI: 10.13193/j.issn.1673-7717.2025.03.004
Bing WU, Liuliu ZHANG, Yong’an GUO, Qijie QIAN, Jing WU. Study on Construction of TCM Syndrome Differentiation Model of Cancer Pain Based on Artificial Intelligence[J]. Chinese Archives of Traditional Chinese Medicine, 2025 , 43 (3) : 19 -23 . DOI: 10.13193/j.issn.1673-7717.2025.03.004
Year 2025 volume 43 Issue 3
40
18
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doi: 10.13193/j.issn.1673-7717.2025.03.004
  • Online Date:2026-10-30
  • Published:2025-03-10
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    1.Jiangsu Cancer Hospital,Jiangsu Institute of Cancer Research,The Affiliated Cancer Hospital of Nanjing Medical University,Nanjing 210009,Jiangsu,China
    2.Nanjing University of Posts and Telecommunications,Nanjing 210023,Jiangsu,China
    3.Jiangsu Nursing Society,Nanjing 210008,Jiangsu,China
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表12种不同金属材料的力学参数
科
Family
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Number of
genus
种数
Number of
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鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
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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