Open Access

ARTICLE

Traditional Chinese Medicine Automated Diagnosis Based on Knowledge Graph Reasoning

Dezheng Zhang^1,2, Qi Jia^1,2, Shibing Yang^1,2, Xinliang Han², Cong Xu³, Xin Liu^1,4, Yonghong Xie^1,2,*

1 School of Computer & Communication Engineering, University of Science & Technology Beijing, Beijing, 100083, China
2 Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 100083, China
3 Inspur Electronic Information Industry Co., Ltd. & State Key Laboratory of High-End Server & Storage Technology, Jinan, 250101, China
4 Surgical Simulation Research Lab, Department of Surgery, University of Alberta, Edmonton, T6G 2E1, Alberta, Canada

* Corresponding Author: Yonghong Xie. Email:

Computers, Materials & Continua 2022, 71(1), 159-170. https://doi.org/10.32604/cmc.2022.017295

Received 26 January 2021; Accepted 01 March 2021; Issue published 03 November 2021

Abstract

Syndrome differentiation is the core diagnosis method of Traditional Chinese Medicine (TCM). We propose a method that simulates syndrome differentiation through deductive reasoning on a knowledge graph to achieve automated diagnosis in TCM. We analyze the reasoning path patterns from symptom to syndromes on the knowledge graph. There are two kinds of path patterns in the knowledge graph: one-hop and two-hop. The one-hop path pattern maps the symptom to syndromes immediately. The two-hop path pattern maps the symptom to syndromes through the nature of disease, etiology, and pathomechanism to support the diagnostic reasoning. Considering the different support strengths for the knowledge paths in reasoning, we design a dynamic weight mechanism. We utilize Naïve Bayes and TF-IDF to implement the reasoning method and the weighted score calculation. The proposed method reasons the syndrome results by calculating the possibility according to the weighted score of the path in the knowledge graph based on the reasoning path patterns. We evaluate the method with clinical records and clinical practice in hospitals. The preliminary results suggest that the method achieves high performance and can help TCM doctors make better diagnosis decisions in practice. Meanwhile, the method is robust and explainable under the guide of the knowledge graph. It could help TCM physicians, especially primary physicians in rural areas, and provide clinical decision support in clinical practice.

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Keywords

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