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Auxiliary Diagnosis Based on the Knowledge Graph of TCM Syndrome

Yonghong Xie1, 3, Liangyuan Hu1, 3, Xingxing Chen2, 3, Jim Feng4, Dezheng Zhang1, 3, *

1 School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
2 School of Automation and Electrical Engineering, University of Science and Technology Beijing, Beijing, 100083, China.
3 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China.
4 Amphenol Global Interconnect Systems, San Jose, CA 95131, USA.

* Corresponding Author: Dezheng Zhang. Email: email.

Computers, Materials & Continua 2020, 65(1), 481-494. https://doi.org/10.32604/cmc.2020.010297

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.

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Cite This Article

Y. Xie, L. Hu, X. Chen, J. Feng and D. Zhang, "Auxiliary diagnosis based on the knowledge graph of tcm syndrome," Computers, Materials & Continua, vol. 65, no.1, pp. 481–494, 2020. https://doi.org/10.32604/cmc.2020.010297

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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