Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling
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 perform...
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| Format: | Article |
| Language: | English |
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Elsevier
2022-06-01
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| Series: | Integrative Medicine Research |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213422021001165 |
| _version_ | 1819097374418534400 |
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| author | Seoyoung Lee Yeonhee Ryu Hi-Joon Park In-Seon Lee Younbyoung Chae |
| author_facet | Seoyoung Lee Yeonhee Ryu Hi-Joon Park In-Seon Lee Younbyoung Chae |
| author_sort | Seoyoung Lee |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T00:14:05Z |
| format | Article |
| id | doaj.art-1c725867cc404779bbba0cac0b07eb12 |
| institution | Directory Open Access Journal |
| issn | 2213-4220 |
| language | English |
| last_indexed | 2024-12-22T00:14:05Z |
| publishDate | 2022-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Integrative Medicine Research |
| spelling | doaj.art-1c725867cc404779bbba0cac0b07eb122022-12-21T18:45:22ZengElsevierIntegrative Medicine Research2213-42202022-06-01112100829Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scalingSeoyoung Lee0Yeonhee Ryu1Hi-Joon Park2In-Seon Lee3Younbyoung Chae4Department of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul, Republic of KoreaKM Fundamental Research Division, Korea Institute of Oriental Medicine, Daejeon, Republic of KoreaDepartment of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul, Republic of KoreaDepartment of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea; Corresponding authors at: Acupuncture and Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, Republic of Korea.Department of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul, Republic of Korea; Corresponding authors at: Acupuncture and Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, 1 Hoegi-dong, Dongdaemun-gu, Seoul, Republic of Korea.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.http://www.sciencedirect.com/science/article/pii/S2213422021001165Acupoint indicationClinical trialsClusteringData miningMultidimensional scaling |
| spellingShingle | Seoyoung Lee Yeonhee Ryu Hi-Joon Park In-Seon Lee Younbyoung Chae Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling Integrative Medicine Research Acupoint indication Clinical trials Clustering Data mining Multidimensional scaling |
| title | Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling |
| title_full | Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling |
| title_fullStr | Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling |
| title_full_unstemmed | Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling |
| title_short | Characteristics of five-phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling |
| title_sort | characteristics of five phase acupoints from data mining of randomized controlled clinical trials followed by multidimensional scaling |
| topic | Acupoint indication Clinical trials Clustering Data mining Multidimensional scaling |
| url | http://www.sciencedirect.com/science/article/pii/S2213422021001165 |
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