@Article{cmc.2020.010297,
AUTHOR = {Yonghong Xie, Liangyuan Hu, Xingxing Chen, Jim Feng, Dezheng Zhang},
TITLE = {Auxiliary Diagnosis Based on the Knowledge Graph of TCM  Syndrome},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {481--494},
URL = {http://www.techscience.com/cmc/v65n1/39578},
ISSN = {1546-2226},
ABSTRACT = {As one of the most valuable assets in China, traditional medicine has a long 
history and contains pieces of knowledge. The diagnosis and treatment of Traditional 
Chinese Medicine (TCM) has benefited from the natural language processing technology. 
This paper proposes a knowledge-based syndrome reasoning method in computerassisted diagnosis. This 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 path. According 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 causes. Moreover, this study applies the Term 
Frequency-Inverse Document Frequency (TF-IDF) idea to the computer-assisted
diagnosis of TCM for the score of syndrome calculation. Finally, 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.},
DOI = {10.32604/cmc.2020.010297}
}