Selection of Acupoints Combination Based on Pseudo Mutual Information Maximization

Jingjing Xu; Yijia Liu; Xiangnan Xu; Kian-Kai Cheng; Zongbao Yang; Jiyang Dong

doi:10.1109/ACCESS.2020.3018504

IEEE Access (Jan 2020)

Selection of Acupoints Combination Based on Pseudo Mutual Information Maximization

Jingjing Xu,
Yijia Liu,
Xiangnan Xu,
Kian-Kai Cheng,
Zongbao Yang,
Jiyang Dong

Affiliations

Jingjing Xu: ORCiD; Department of Electronic Science, Fujian Provincial Key Laboratory for Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Yijia Liu: Department of Electronic Science, Fujian Provincial Key Laboratory for Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Xiangnan Xu: ORCiD; Department of Electronic Science, Fujian Provincial Key Laboratory for Plasma and Magnetic Resonance, Xiamen University, Xiamen, China
Kian-Kai Cheng: ORCiD; Innovation Center in Agritechnology, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
Zongbao Yang: Department of Chinese Medicine, Xiamen University, Xiamen, China
Jiyang Dong: ORCiD; Department of Electronic Science, Fujian Provincial Key Laboratory for Plasma and Magnetic Resonance, Xiamen University, Xiamen, China

DOI: https://doi.org/10.1109/ACCESS.2020.3018504
Journal volume & issue: Vol. 8
pp. 153707 – 153718

Abstract

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Acupuncture has been used as an alternative and complementary therapy, however the selection of acupoints combination for a specific disease (or symptoms) highly depends on the experience of acupuncturists. The current study aimed to develop a reliable and quantifiable method for selection of acupoints combinations, which may improve treatment efficiency and reproducibility among acupuncturists. In the current study, a number of acupuncture prescriptions for treatment of acute or chronic gastritis had been collected and organized from public databases and literatures. Three types of associations, acupoints-symptoms associations (ASA), acupoints-acupoints associations (AAA), and symptoms-symptoms associations (SSA) were defined to delineate their connections. The network constructed by AAA was used to study the synergistic effects among acupoints combination whereas SSA network was used to examine coexistence of symptoms. On the other hand, ASA characterized the empirical association between acupoints and symptoms in the prescription database which may be considered as the mathematical representation of acupoints selection for a particular prescription. Then a novel method namely mutual information screening (MIS) was proposed for screening acupoints combinations based on pseudo mutual information maximization. The selected acupoints combinations of MIS method were verified to be in accordance with the rules of acupoints compatibility based on the theory of acupuncture. Validation of MIS through conditional entropy and significance test of the clinical records suggested the method to be reliable and robust. Furthermore, a ten-fold cross validation were carried out to evaluate the performance of MIS compared with artificial neural network (ANN)-based method. The F1-score of MIS was found to be 0.67, higher than that of the ANN-based method (0.58). The current results provided a reliable and practical tool for symptom-specific acupoints selection, which may help to develop a scientific basis for clinical acupuncture therapy.

Published in IEEE Access

ISSN: 2169-3536 (Online)
Publisher: IEEE
Country of publisher: United States
LCC subjects: Technology: Electrical engineering. Electronics. Nuclear engineering
Website: https://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6287639

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