Classifying Syndromes in Chinese Medicine Using Multi-label Learning Algorithm with Relevant Features for Each Label  

作　　者：徐璡 许朝霞 陆萍 郭睿 燕海霞 许文杰 王忆勤 夏春明 

机构地区：[1]School of Basic Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai 201203, China [2]Center for Mechatronics Engineering, East China University of Science and Technology, Shanghai 200237, China

出　　处：《Chinese Journal of Integrative Medicine》2016年第11期867-871,共5页中国结合医学杂志（英文版）

基　　金：Supported by the National Natural Science Foundation of China(No.81173199);Shanghai Sailing Program(No.15YF1412100);Young Teachers' Training Funded Project in Shanghai University(No.ZZszy13003);Budget for Research Shanghai Municipal Education Commission(No.2013JW06);China

摘　　要：Objective: To develop an effective Chinese Medicine(CM) diagnostic model of coronary heart disease(CHD) and to confirm the scientific validity of CM theoretical basis from an algorithmic viewpoint. Methods: Four types of objective diagnostic data were collected from 835 CHD patients by using a selfdeveloped CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm(REAL). Results: REAL was employed to establish a Xin(Heart) qi deficiency, Xin yang deficiency, Xin yin deficiency, blood stasis, and phlegm five-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. Conclusions: The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.Objective: To develop an effective Chinese Medicine(CM) diagnostic model of coronary heart disease(CHD) and to confirm the scientific validity of CM theoretical basis from an algorithmic viewpoint. Methods: Four types of objective diagnostic data were collected from 835 CHD patients by using a selfdeveloped CM inquiry scale for the diagnosis of heart problems, a tongue diagnosis instrument, a ZBOX-I pulse digital collection instrument, and the sound of an attending acquisition system. These diagnostic data was analyzed and a CM diagnostic model was established using a multi-label learning algorithm(REAL). Results: REAL was employed to establish a Xin(Heart) qi deficiency, Xin yang deficiency, Xin yin deficiency, blood stasis, and phlegm five-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. Conclusions: The multi-label learning method established using four diagnostic models based on mutual information feature selection yielded good recognition results. The characteristic model parameters were selected by maximizing the mutual information for each card type. The four diagnostic methods used to obtain information in CM, i.e., observation, auscultation and olfaction, inquiry, and pulse diagnosis, can be characterized by these parameters, which is consistent with CM theory.

关 键 词：Chinese medicine syndrome differentiation multi-label learning algorithm 

分 类 号：R259[医药卫生—中西医结合]