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Application and progress of data mining in study of compatibility law of traditional Chinese medicineChinese Full TextEnglish Full Text (MT)

LIU Meng-ling;ZHANG Xin-you;DING Liang;PAN Shu-mao;WU Di-yao;LI Xiu-yun;College of Computer Science, Jiangxi University of Chinese Medicine;School of Pharmacy, Jiangxi University of Chinese Medicine;

Abstract: Data mining is an important method to obtain the key information from a large amount of data, and it is widely applied in the research on the modernization of traditional Chinese medicine(TCM). The compatibility law of herbs is a key issue in the research of TCM prescriptions. This reflects the flexibility and effectiveness of TCM prescriptions, and it is also a crucial link to the development of TCM modernization. Therefore, it is the core purpose of the research on TCM prescriptions to find the compatibility law of herbs and clarify the scientific connotation. Data mining, as an effective method and an important approach, has formed a standardized system in the research of compatibility law of herbs, which can reveal the relationship between different Chinese herbs and summarize the internal rules in compatibility. Two hundred and twenty two effective papers were sorted out and categorized in this article. The results showed that data mining was mainly applied in finding the core Chinese herb pairs, summarizing the utility and attributes of TCM prescriptions, revealing the relationship between prescriptions, Chinese herbs and syndromes, finding the optimal dose of Chinese herbs, and producing the new prescriptions. The problems of data mining in research of herbs compatibility rules were summarized, and its development and trend in current researches were discussed in this article to provide useful references for the in-depth study of data mining in the compatibility law of Chinese herbs.
  • DOI:

    10.19540/j.cnki.cjcmm.20210303.501

  • Series:

    (E) Medicine & Public Health

  • Subject:

    Traditional Chinese Medicinal Herbs

  • Classification Code:

    R289.1

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