文献类型：期刊文献

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

英文题名：Classifying syndromes in Chinese medicine using multi-label learning algorithm with relevant features for each label

作者：Xu Jin[1];Xu Zhao-xia[1];Lu Ping[2];Guo Rui[1];Yan Hai-xia[1];Xu Wen-jie[1];Wang Yi-qin[1];Xia Chun-ming[2]

机构：[1]Shanghai Univ Tradit Chinese Med, Sch Basic Med, Shanghai 201203, Peoples R China;[2]East China Univ Sci & Technol, Ctr Mechatron Engn, Shanghai 200237, Peoples R China

年份：2016

卷号：22

期号：11

起止页码：867

中文期刊名：Chinese Journal of Integrative Medicine

外文期刊名：CHINESE JOURNAL OF INTEGRATIVE MEDICINE

收录：;Scopus;WOS:【SCI-EXPANDED(收录号:WOS:000387149800011)】；CSCD:【CSCD2015_2016】；PubMed;

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) and Budget for Research Shanghai Municipal Education Commission (No. 2013JW06), China

语种：英文

中文关键词：Chinese medicine;syndrome differentiation;multi-label learning algorithm

外文关键词：Chinese medicine; syndrome differentiation; multi-label learning algorithm

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.
To develop an effective Chinese Medicine (CM) diagnostic model of coronary heart disease (CHD) and to confifirm the scientifific validity of CM theoretical basis from an algorithmic viewpoint. Four types of objective diagnostic data were collected from 835 CHD patients by using a self-developed 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). REAL was employed to establish a Xin (Heart) qi defificiency, Xin yang defificiency, Xin yin defificiency, blood stasis, and phlegm fifive-card CM diagnostic model, which had recognition rates of 80.32%, 89.77%, 84.93%, 85.37%, and 69.90%, respectively. 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.