Science & Technology Review
|
2024, 42(21): 66-72
• Exclusive: The paradigm and application of clinical research in traditional Chinese medicine •
Research on inheritance and transformation methods of famous classical prescriptions based on medical records data
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FANG Yuxuan, SHAO Mingyi, ZHANG Rongrong, ZHAO Ruixia, LIU Yanan, CUI Hongyan
Affiliations
Published: 2024-11-13
doi: 10.3981/j.issn.1000-7857.2024.05.00469
Outline
The famous classical formulas(FCF) is a widely used prescription with definite curative effect and obvious characteristics and advantages recorded in ancient Chinese medicine books, which were accumulated with extensive human experience in clinical practice and were mostly presented in case form.By analyzing the development of FCF cases in inheritance, it was found that there were problems such as excessive emphasis on individual experience summarization, lack of systematic sorting, inappropriate matching of the current research model, lack of high-quality evidence, and insufficient exploration of tacit knowledge, which limited the inheritance and development of FCF.In the framework of real world study, the team proposed combining big data technology with the inheritance of FCF through the cognitive hierarchy model of the human brain, "data-information-knowledge-wisdom, " that is, structuring data, information, evaluating case records, uncovering tacit knowledge, and presenting knowledge maps, ultimately realizing the reconstruction of classical prescription case records and the transformation of assisted decision-making.
traditional Chinese medicine
/
real world study
/
big data
/
famous classical formula
FANG Yuxuan, SHAO Mingyi, ZHANG Rongrong, ZHAO Ruixia, LIU Yanan, CUI Hongyan.
Research on inheritance and transformation methods of famous classical prescriptions based on medical records data[J].
Science & Technology Review,
2024
, 42
(21)
: 66
-72
.
DOI: 10.3981/j.issn.1000-7857.2024.05.00469
Year 2024 volume 42 Issue 21
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1048
492
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Article Info
doi: 10.3981/j.issn.1000-7857.2024.05.00469
- Receive Date:2024-04-21
- Online Date:2024-12-14
- Published:2024-11-13