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KMID : 1150720220110020009
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Integrative Medicine Research
2022 Volume.11 No. 2 p.9 ~ p.9
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Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling
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Lee Seo-Young
Ryu Yeon-Hee
Park Hi-Joon
Lee In-Seon
Chae Youn-Byoung
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Abstract
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Background: An unbiased assessment of clinical outcomes may provide greater insight into the characteristics of individual acupoints. In this study, we used machine-learning methods to examine clinical trial data for diseases treated using prescribed five-phase acupoint patterns.
Methods: We performed a search of acupuncture treatment regimens used in randomized controlled trials included in the Cochrane Database of Systematic Reviews. The frequencies of 60 five-phase acupoints were calculated based on 421 clinical trials on 30 diseases. The characteristics of prescribed five-phase acupoints were further analyzed using multidimensional scaling and K-means clustering.
Results: Among the five-phase acupoints, stream and sea acupoints were the most widely used, with well, spring, and river acupoints less common. Multidimensional scaling and cluster analysis revealed that the LR3, ST36, GB34, BL60, KI3, LI11, and HT7 acupoints exhibited distinct characteristics based on distances representing the similarity between acupoint indications.
Conclusions: The results suggest that stream and sea acupoints exhibit distinct characteristics compared to the other acupoints. Such data-driven approaches will improve our understanding of five-phase acupoints and facilitate the establishment of new models of analysis and educational resources for major acupoint characteristics.
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KEYWORD
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Acupoint indication, Clinical trials, Clustering, Data mining, Multidimensional scaling
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