A knowledge graph based intelligent auxiliary diagnosis and treatment system for primary tinnitus using traditional Chinese medicine

Abstract

Primary tinnitus is a disabling disease with an unknown pathogenesis and a high incidence rate in China. Its diagnosis and treatment are complex and difficult to control. Although many treatments are available for primary tinnitus, their efficacy is often unsatisfactory. This paper proposes a new diagnosis and treatment method using knowledge graphs, and an intelligent assistant decision system is developed. To support diagnosis, a knowledge graph is created as a decision support tool using traditional Chinese medicine (TCM). Based on the knowledge graph, a model for the syndrome differentiation of tinnitus in TCM is built. At tinnitus treatment, an intelligent recommandation model for pentatonic music using knowledge graph based heterogeneous label propagation is then used to provide patients with personalized treatment plans. According to evaluation results, the proposed method achieves an accuracy of 87.1 % in tinnitus diagnosis. Compared with the control group, the recommended pentatonic music had a more obvious effect, and the efficacy of the five types of tinnitus was increased by 33.34 %, 33.33 %, 20 %, 26.67 %, 33.34 %, respectively. The system developed in this paper will help clinicians improve the diagnosis and treatment of tinnitus while reducing unnecessary medical expenses and offering significant social and economic benefits.