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Frontiers of Medicine

ISSN 2095-0217

ISSN 2095-0225(Online)

CN 11-5983/R

Postal Subscription Code 80-967

2018 Impact Factor: 1.847

Front. Med.    2019, Vol. 13 Issue (1) : 112-120    https://doi.org/10.1007/s11684-017-0582-z
RESEARCH ARTICLE |
Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model
Won-Mo Jung1, In-Soo Park2, Ye-Seul Lee1,2, Chang-Eop Kim3, Hyangsook Lee1,2, Dae-Hyun Hahm4, Hi-Joon Park1,2, Bo-Hyoung Jang5, Younbyoung Chae1,2()
1. Department of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul 130-701, Republic of Korea
2. Acupuncture & Meridian Science Research Center, College of Korean Medicine, Kyung Hee University, Seoul 130-701, Republic of Korea
3. Department of Physiology, College of Korean Medicine, Gachon University, Seoul 131-120, Republic of Korea
4. Department of Physiology, School of Medicine, Kyung Hee University, Seoul 130-701, Republic of Korea
5. Department of Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul 130-701, Republic of Korea
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Abstract

Comprehension of the medical diagnoses of doctors and treatment of diseases is important to understand the underlying principle in selecting appropriate acupoints. The pattern recognition process that pertains to symptoms and diseases and informs acupuncture treatment in a clinical setting was explored. A total of 232 clinical records were collected using a Charting Language program. The relationship between symptom information and selected acupoints was trained using an artificial neural network (ANN). A total of 11 hidden nodes with the highest average precision score were selected through a tenfold cross-validation. Our ANN model could predict the selected acupoints based on symptom and disease information with an average precision score of 0.865 (precision, 0.911; recall, 0.811). This model is a useful tool for diagnostic classification or pattern recognition and for the prediction and modeling of acupuncture treatment based on clinical data obtained in a real-world setting. The relationship between symptoms and selected acupoints could be systematically characterized through knowledge discovery processes, such as pattern identification.

Keywords acupuncture      indication      neural network      pattern identification      prediction     
Corresponding Authors: Younbyoung Chae   
Just Accepted Date: 27 December 2017   Online First Date: 12 April 2018    Issue Date: 12 March 2019
 Cite this article:   
Won-Mo Jung,In-Soo Park,Ye-Seul Lee, et al. Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model[J]. Front. Med., 2019, 13(1): 112-120.
 URL:  
http://academic.hep.com.cn/fmd/EN/10.1007/s11684-017-0582-z
http://academic.hep.com.cn/fmd/EN/Y2019/V13/I1/112
Fig.1  (A) “Charting Language” program. This system uses specific syntax and medical terms unconstrained by the structure inherent to a defined language. A total of 232 clinical records were collected for neural network analysis. (B) ANN model. A feed-forward network that consists of input, hidden, and output layers was employed as an ANN model to learn the acupuncture treatment patterns in Korean medicine. A total of 87 nodes in the input layer corresponded to 87 symptoms present among the medical records (layer with blue circles). The number of hidden nodes was 11 based on the highly predictive performance achieved through a tenfold cross validation (layer with green circles). A total of 77 nodes in the output layer correspond to 77 acupoints identified from the medical records (layer with red circles).
No. Most frequently observed symptoms Most frequently used acupoints
1 Back pain (14.8%) LI11 (7.9%)
2 Shoulder pain (9.5%) LU8 (5.3%)
3 Strained muscle (4.5%) SP3 (4.8%)
4 Knee pain (4.1%) LR4 (4.5%)
5 Lower limb pain (4.1%) BL23 (4.4%)
6 Upper limb pain (3.7%) BL24 (4.4%)
7 Edema (3.6%) BL26 (4.3%)
8 Neck pain (3.6%) GB43 (4.3%)
9 Dyspepsia (2.9%) ST36 (4.1%)
10 Ankle pain (2.8%) KI10 (3.9%)
11 Finger joint pain (2.6%) LU9 (3.9%)
12 Scapula pain (2.6%) LR8 (3.8%)
13 Pressure pain on BL24 (2.1%) TE6 (2.5%)
14 Arthralgia (1.8%) TE15 (2.4%)
15 Foot pain (1.8%) LI1 (2.1%)
16 Stiff neck (1.8%) GB41 (2.0%)
17 Wrist pain (1.8%) SI14 (2.0%)
18 Insomnia (1.7%) SP9 (1.7%)
19 Headache (1.6%) ST43 (1.7%)
20 Pressure pain on BL26 (1.6%) LI5 (1.7%)
Tab.1  Most frequently observed symptoms and acupoints of patients in 232 clinical records
No. Most frequently used acupoint combinations
1 SI12–SI18 (n = 38)
2 SI12–LI4 (n = 38)
3 SI18–LI4 (n = 38)
4 SP10–LI5 (n = 38)
5 TE15–SP10 (n = 38)
6 TE15–LI5 (n = 38)
7 EX-LE4–SP10 (n = 37)
8 SI12–SI2 (n = 32)
9 SI12–SP10 (n = 31)
10 SI18–SI2 (n = 31)
11 SI2–LI4 (n = 31)
12 SI18–SP10 (n = 30)
13 SP10–LI4 (n = 30)
14 SI2–SP10 (n = 29)
15 EX-LE4–TE15 (n = 23)
16 EX-LE4–LI5 (n = 23)
17 BL23–SP10 (n = 22)
18 EX-LE4–SI2 (n = 20)
19 LR2–SP10 (n = 20)
20 LR2–BL23 (n = 20)
Tab.2  Most frequently used acupoint combinations in 232 clinical records
Fig.2  Network analysis of acupoint combinations. The collected data contained 232 acupoint prescriptions. Acupoint combination network is generated with nodes (acupoints) and edges (co-occurrences between two acupoints). Networkx 1.11.0 (Python library) is used to construct the network, and Gephi 0.8.2 is utilized to visualize the network. The investigation of the visual pattern of the network is facilitated by intuitive and comprehensive analyses. The acupoint combination network contained 52 acupoints. The color of the node represents eigenvector centrality (colors closer to red represent higher values). Edge thickness is proportional to the frequency of the co-occurrences among linked nodes.
Fig.3  Selected acupoints for 232 cases were represented in a color-coded matrix. (A) Estimated acupoints from the fully fitted ANN model (average precision score, 0.865; precision, 0.911; recall, 0.811). (B) Actual acupoint selections in 232 cases. Selected acupoints were coded in black in the two matrices. (C) Correct estimations (black), false positive errors (blue), and false negative errors (red).
Fig.4  Network visualization of relationships among symptoms, acupoints, and hidden nodes based on the correlation of activity patterns. The five symptoms and acupoints that show the strongest correlations with the activity patterns of the 11 hidden nodes were extracted. In total, 11 hidden nodes were associated with 22 symptoms and 26 acupoints (5 element acupoints are marked with a red “#.”). Line widths are proportional to the size of the correlation coefficients that describe the relationships among connected nodes.
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