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, 2014, 329563

Data Mining of Acupoint Characteristics From the Classical Medical Text: DongUiBoGam of Korean Medicine


Data Mining of Acupoint Characteristics From the Classical Medical Text: DongUiBoGam of Korean Medicine

Taehyung Lee et al. Evid Based Complement Alternat Med.


Throughout the history of East Asian medicine, different kinds of acupuncture treatment experiences have been accumulated in classical medical texts. Reexamining knowledge from classical medical texts is expected to provide meaningful information that could be utilized in current medical practices. In this study, we used data mining methods to analyze the association between acupoints and patterns of disorder with the classical medical book DongUiBoGam of Korean medicine. Using the term frequency-inverse document frequency (tf-idf) method, we quantified the significance of acupoints to its targeting patterns and, conversely, the significance of patterns to acupoints. Through these processes, we extracted characteristics of each acupoint based on its treating patterns. We also drew practical information for selecting acupoints on certain patterns according to their association. Data analysis on DongUiBoGam's acupuncture treatment gave us an insight into the main idea of DongUiBoGam. We strongly believe that our approach can provide a novel understanding of unknown characteristics of acupoint and pattern identification from the classical medical text using data mining methods.


Figure 1
Figure 1
(a) The ShinHyungJangBuDo (chart of the overall body, viscera, and bowels) at the opening of the NaeGyeong chapter represents the medical perspective of DongUiBoGam. Heo Jun tried to express the common thread of this book of maintaining health through this picture; the circulation (“ascending of essence and qi” and “descending of fire”) of the body. Wiggling nose, opened mouth, and moving belly show the descending of “fire” through deep breath, and prominent spine and brain represent the ascending of “essence and qi” through meditation. (b) Acupuncture and Moxibustion Methods of subchapter “qi” in the NaeGyeong chapter of DongUiBoGam. The acupoint methods in subchapter “qi” contain the most frequently used acupoints: CV4, CV6, and ST36.
Figure 2
Figure 2
The tf-idf(p,a) matrix. In this matrix, each acupoint (sample) is represented by a vector of tf-idf(p,a) weights of 25 patterns in the three pattern identification categories (feature). To compare acupoints in the vector space, the tf-idf weights of each acupoint were normalized for the same length by the cosine normalization (1/w12+w22+w32+wM2). The 43 acupoints are presented on the x-axis of the array. The most frequently used acupoints were CV4 and CV6 (16 times each), ST36 (13 times), and SP6 and CV3 (12 times each). On the y-axis in Figure 2, 25 patterns are presented. The three pattern identification categories are shown in different colors on the y-axis (green: five essential components of the human body; yellow: viscera and bowels; orange: internal damage and external contractions).
Figure 3
Figure 3
A network model of acupoints defined by tf-idf values with 25 patterns in the three pattern identification categories. (a) Radar charts showing characteristics of module number 6 (acupoints CV4, SP9, CV6, LR1, and KI10) in the network model. (b) Radar charts showing characteristics of module number 3 (acupoints CV3, SP6, and BL23) in the network model. Based on the charts, the characteristics of each module's acupoints can be understood. Acupoints CV6 and CV4 were both in the number six module. Acupoint CV4 acts as a hub among the other acupoints (SP9, CV6, and KI10). When evaluating the number six module, acupoint CV4 appears to play a key role. Acupoint CV4, with other acupoints in the number six module, has high values in the patterns “fire,” “small intestine,” “qi,” “bladder,” and “kidney.” On the other hand, acupoints CV3 and SP6 were in the number three module.
Figure 4
Figure 4
The tf-idf(a,p) matrix. In the case of tf-idf(a,p), all were calculated in the other way around. Using tf-idf(a,p) values, each pattern id was represented as a vector in 114 dimensional vector spaces (the number of acupoints). In this matrix, each pattern (sample) is represented by a vector of tf-idf(a,p) weights of 43 acupoints (feature). The most frequently used patterns in the NaeGyeong chapter of DongUiBoGam were “fire” (94 times), “qi” (89 times), “blood” (46 times), “phlegm” (39 times), and “heart” (34 times). Regarding “fire,” CV4 (tf-idf: 0.42), SP6 (tf-idf: 0.31), CV3 (tf-idf: 0.31), CV6 (tf-idf: 0.30), and SP9 (tf-idf: 0.23) were analyzed as applicable acupoints. Analysis of the pattern “qi” showed that acupoints CV4 (tf-idf: 0.38), SP6 (tf-idf: 0.28), CV6 (tf-idf: 0.23), CV3 (tf-idf: 0.23), and SP9 (tf-idf: 0.22) had high tf-idf values. Notably, the top two patterns, “fire” and “qi,” shared the same top five acupoints.

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