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Data-driven quantification and intelligent decision-making in traditional Chinese medicine: a review

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Abstract

Traditional Chinese medicine (TCM) originates from the practical experience of human beings’ constant struggle with nature. In five thousand years, TCM has gradually risen from empirical medicine to modern evidence-based medicine with complete scientific principles such as fundamental systematic theories, treatment principles and methods, classic prescriptions, famous medicines. The development of information science, data science, and computer technology has provided effective models, methods, and technologies for modern TCM’s quantitative and intelligent diagnosis and treatment decision-making. And it also has promoted the development of TCM from evidence-based medicine to intelligent TCM. Starting from the development of TCM, we introduce the rise and connotation of ancient, modern, and intelligent TCM. Moreover, we emphatically analyze the research status of quantification and intelligent decisions for the whole disease cycle, including data-driven modern TCM diagnosis, program optimization, and treatment program evaluation. In addition, we discuss the critical issues of data-driven TCM quantification and intelligent decision research and briefly elaborate on the new ideas of data-driven intelligent TCM research. In conclusion, compared with traditional research paradigms, the advantages of data-driven medical decision research paradigms are as follows: (1) From the perspective of decision-making subjects, the data-driven research paradigm describes the clinical decision-making mechanism in real scenarios with rigorous mathematical theories, which will break through the difference between the conclusions drawn by clinical design research methods and clinical practice. (2) By applying the results of basic theoretical research to clinical decision-making practice in real scenarios, the data-driven medical decision-making research paradigm will contribute to getting out of the dilemma that the conclusions drawn by traditional AI models are difficult to explain in clinical practical decision-making.

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Data availability

The data that support the findings of this study are available from the corresponding author upon resonable request.

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Acknowledgements

The work is partly supported by the National Natural Science Foundation of China (72301082 and 72071152 ), Guangdong Basic and Applied Basic Research Foundation (2022A1515110703), The Guangdong Provincial Hospital of Chinese Medicine Science and Technology Research Project (YN2022QN33), Shaanxi National Funds for Distinguished Young Scientists (2023-JC-JQ-11), the Youth Innovation Team of Shaanxi Universities (2019), The Guangzhou Key Research and Development Program (202206010101).

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Authors and Affiliations

  1. State Key Laboratory of Traditional Chinese Medicine Syndrome/Department of TCM Big Data Research, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China

    Xiaoli Chu

  2. The Second Clinical Medical School, Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China

    Simin Wu

  3. School of Economics and Management, Xidian University, Xi’an, 710071, Shaanxi, China

    Bingzhen Sun

  4. Department of Rheumatology, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510120, Guangdong, China

    Qingchun Huang

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  1. Xiaoli Chu
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  2. Simin Wu
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  3. Bingzhen Sun
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  4. Qingchun Huang
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Correspondence to Bingzhen Sun or Qingchun Huang.

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Chu, X., Wu, S., Sun, B. et al. Data-driven quantification and intelligent decision-making in traditional Chinese medicine: a review. Int. J. Mach. Learn. & Cyber. (2024). https://doi.org/10.1007/s13042-024-02103-9

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