Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling

Abstract

BackgroundAn 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. MethodsWe 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. ResultsAmong 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. ConclusionsThe 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.