Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome

作     者：Yonghong Xie Liangyuan Hu Xingxing Chen Jim Feng Dezheng Zhang 

作者机构：School of Computer and Communication EngineeringUniversity of Science and Technology BeijingBeijing100083China School of Automation and Electrical EngineeringUniversity of Science and Technology BeijingBeijing100083China Beijing Key Laboratory of Knowledge Engineering for Materials ScienceBeijing100083China Amphenol Global Interconnect SystemsSan JoseCA 95131USA 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2020年第65卷第10期

页      面：481-494页

核心收录：

学科分类：0831[工学-生物医学工程（可授工学、理学、医学学位）] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 070101[理学-基础数学] 

基　　金：Supported by the National Key Research and Development Program of China under Grant 2017YFB1002304 and the National Natural Science Foundation of China(No.61672178) The author who received the grant is Azguri,and the official website of the funder is http://www.most.gov.cn/ 

主　　题：Knowledge graph reinforcement learning auxiliary diagnosis inference path 

摘      要：As one of the most valuable assets in China,traditional medicine has a long history and contains pieces of *** diagnosis and treatment of Traditional Chinese Medicine(TCM)has benefited from the natural language processing *** paper proposes a knowledge-based syndrome reasoning method in computer-assisted *** method is based on the established knowledge graph of TCM and this paper introduces the reinforcement learning algorithm to mine the hidden relationship among the entities and obtain the reasoning *** to this reasoning path,we could infer the path from the symptoms to the syndrome and get all possibilities via the relationship between symptoms and ***,this study applies the Term Frequency-Inverse Document Frequency(TF-IDF)idea to the computer-assisted diagnosis of TCM for the score of syndrome ***,combined with symptoms,syndrome,and causes,the disease could be confirmed comprehensively by voting,and the experiment shows that the system can help doctors and families to disease diagnosis effectively.