Progress and prospect of machine learning in research of acupuncture and moxibustion.
10.13703/j.0255-2930.20191026-0002
- Author:
Tao YIN
1
;
Zhao-Xuan HE
1
;
Rui-Rui SUN
1
;
Zheng-Jie LI
1
;
Si-Yi YU
1
;
Lei LAN
2
;
Jian HOU
3
;
Fang ZENG
2
Author Information
1. School of Acupuncture-Moxibustion and Tuina, Chengdu University of TCM, Chengdu 610075, Sichuan Province, China; Acupuncture Brain Science Research Center, Chengdu University of TCM, Chengdu 610075, Sichuan Province, China.
2. School of Acupuncture-Moxibustion and Tuina, Chengdu University of TCM, Chengdu 610075, Sichuan Province, China; Acupuncture Brain Science Research Center, Chengdu University of TCM, Chengdu 610075, Sichuan Province, China; Key Laboratory of Acupuncture-Moxibustion and Chronobiology, Chengdu
3. Acupuncture Brain Science Research Center, Chengdu University of TCM, Chengdu 610075, Sichuan Province, China; Department of Radiology, Affiliated Hospital of Chengdu University of TCM.
- Publication Type:Journal Article
- Keywords:
acupuncture and moxibustiona;
artificial intelligencea;
machine learninga;
neuroimaging
- MeSH:
Acupuncture;
Acupuncture Points;
Acupuncture Therapy;
Machine Learning;
Moxibustion
- From:
Chinese Acupuncture & Moxibustion
2020;40(12):1383-1386
- CountryChina
- Language:Chinese
-
Abstract:
The application progress of machine learning in research of acupuncture and moxibustion was reviewed from three aspects: mining of acupuncture and moxibustion prescription and indications, acupuncture efficacy prediction and its influencing factors, acupoint specificity and acupuncture manipulation research, and the existing problems in current research and future research trends were discussed. It is believed that the appropriate machine learning algorithm should be selected to build the model according to the research purpose and data characteristics in the future research; attention should be paid to feature design, feature selection and feature cleaning; sample data collection should be a priority, and data sharing platform and standardized data collection should be developed to improve the data quality.
