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Journal of Traditional Chinese Medicine >

2025 , Vol. 45 >Issue 1: 192 - 200

DOI: https://doi.org/10.19852/j.cnki.jtcm.2025.01.019

Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine: a case study on resistant hypertension

  • Qianzi CHE ,
  • Dasheng LIU ,
  • Xinghua XIANG ,
  • Yaxin TIAN ,
  • Feibiao XIE ,
  • Wenyuan XU ,
  • Jian LIU ,
  • Xuejie WANG ,
  • Liying WANG ,
  • Weiguo BAI ,
  • Xuejie HAN ,
  • Wei YANG
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  • 1 Department of Medical Statistics, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
    2 Department of Science and Education, Medical Statistics Teaching and Research Office, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
    3 Traditional Chinese Medicine Standards Research Center, Medical Statistics Teaching and Research Office, Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, China
    4 Computer Department, Xiyuan Hospital of the China Academy of Chinese Medical Sciences, Beijing 100091, China

Received date: 2024-01-22

  Accepted date: 2024-04-15

  Online published: 2025-01-10

Supported by

the China Academy of Chinese Medical Sciences, Independent Topic Project: Application Research on Named Entity Recognition and Relationship Extraction of Case Records of Renowned Traditional Chinese Medicine Practitioners(Z0643);China Academy of Chinese Medical Sciences, Independent Topic Project: Analysis of Research Directions and Scope in the Discipline of Traditional Chinese Medicine Statistics(Z0723);China Academy of Chinese Medical Sciences, Science and Technology Innovation Project: Real-world Effectiveness Evaluation of Traditional Chinese Medicine and Translational Application Research on Causal Inference(CI2021A04706);China Academy of Chinese Medical Sciences, Science and Technology Innovation Project: Research on Causal Inference Methodology for Real-world Clinical Evaluation in Traditional Chinese Medicine(CI2021B003);National Key Research and Development Program of China: Integrated Evaluation Model and Key Technologies of "Syndrome-Disease-Prescription" for Traditional Chinese Medicine in the Prevention and Treatment of Coronary Heart Disease — Statistical Data Analysis and Data Mining(2017YFC1700406-2)

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Abstract

OBJECTIVE: To enhance the understanding of identifying personalized pharmacotherapy options in Traditional Chinese Medicine (TCM), and further support the registration of new TCM drugs.

METHODS: Generalized Boosted Models and XGBoost were employed to construct a classification model to identify the bad prognosis factors in resistant hypertension (RH) patients. Furthermore, we used association analysis to explore the rules of "symptom-syndrome" and "symptom-herb" for the major influencing factors, in order to summarize prescription pattern and applicable patients of TCM.

RESULTS: Patients with major adverse cardiac events mostly have complex symptoms of phlegm, stasis, deficiency and fire intermingled with each other, and finally summarized the human experience of using Chinese herbal medicine to precisely intervene in some symptoms of RH patients on the basis of conventional Western medical treatment.

CONCLUSIONS: Machine learning algorithms can make full use of human use experience and evidence to save clinical trial resources and accelerate the development of TCM varieties.

Key words： machine learning; human clinical experience; personalized pharmacotherapy; new drugs registration

Cite this article

Qianzi CHE , Dasheng LIU , Xinghua XIANG , Yaxin TIAN , Feibiao XIE , Wenyuan XU , Jian LIU , Xuejie WANG , Liying WANG , Weiguo BAI , Xuejie HAN , Wei YANG . Integrating machine learning and human use experience to identify personalized pharmacotherapy in Traditional Chinese Medicine: a case study on resistant hypertension[J]. Journal of Traditional Chinese Medicine, 2025 , 45(1) : 192 -200 . DOI: 10.19852/j.cnki.jtcm.2025.01.019

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