Selection of Acupoints Combination Based on Pseudo Mutual Information Maximization

Abstract

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), acupointsacupoints 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.