Knowledge Graph and its Application in the Study of Neurological and Mental Disorders

Qizheng Wang ¹ Fan Yang ¹ Lijie Quan ¹ Mengjie Fu ¹ Zhongli Yang ² Ju Wang ^1*

• ¹ Tianjin Medical University, Tianjin, China
• ² Zhejiang University, Hangzhou, Zhejiang Province, China

Neurological disorders (e.g., Alzheimer's disease and Parkinson's disease) and mental disorders (e.g., depression and anxiety), pose huge challenges to global public health. The pathogenesis of these diseases can usually be attributed to many factors, such as genetic, environmental and socioeconomic status, which make the diagnosis and treatment of the diseases difficult. As research on the diseases advances, so does the body of medical data. The accumulation of such data provides unique opportunities for the basic and clinical study of these diseases, but the vast and diverse nature of the data also make it difficult for physicians and researchers to precisely extract the information and utilize it in their work. A powerful tool to extract the necessary knowledge from large amounts of data is knowledge graph (KG). KG, as an organized form of information, has great potential for the study neurological and mental disorders when it is paired with big data and deep learning technologies. In this study, we reviewed the application of KGs in common neurological and mental disorders in recent years. We also discussed the current state of medical knowledge graphs, highlighting the obstacles and constraints that still need to be overcome.