Characterization of hidden rules linking symptoms and selection of acupoint using an artificial neural network model.
10.1007/s11684-017-0582-z
- Author:
Won-Mo JUNG
1
;
In-Soo PARK
2
;
Ye-Seul LEE
1
;
Chang-Eop KIM
3
;
Hyangsook LEE
1
;
Dae-Hyun HAHM
2
;
Hi-Joon PARK
1
;
Bo-Hyoung JANG
4
;
Younbyoung CHAE
5
Author Information
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 Preventive Medicine, College of Korean Medicine, Kyung Hee University, Seoul, 130-701, Republic of Korea.
5. Department of Science in Korean Medicine, Graduate School, Kyung Hee University, Seoul, 130-701, Republic of Korea. ybchae@khu.ac.kr.
- Publication Type:Journal Article
- Keywords:
acupuncture;
indication;
neural network;
pattern identification;
prediction
- MeSH:
Acupuncture Points;
Acupuncture Therapy;
Humans;
Neural Networks (Computer);
Republic of Korea;
Syndrome
- From:
Frontiers of Medicine
2019;13(1):112-120
- CountryChina
- Language:English
-
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.
