Construction of Migraine Knowledge Graph Based on Diagnosis and Treatment Guidelines of Integrative Medicine

Abstract: Our objective was to transform the text data in migraine diagnosis and treatment guidelines into a visualized knowledge graph by using knowledge graph technology, visually display the clinical diagnosis and treatment process, and provide assistance for the standardization of diagnosis and treatment of migraine with integrative medicine. Manual extraction, knowledge fusion, and standardization were performed on the text data in the migraine diagnosis and treatment guidelines. Combined with the clinical diagnosis and treatment process, the data of Chinese and Western medicine diagnosis and treatment of migraine were visualized and stored through the Neo4j graph database. A knowledge graph of Chinese and Western medicine diagnosis and treatment of migraine including 616 entities and 615 entity relationships was constructed, which realized the visual display of the diagnosis and treatment process of integrative medicine for migraine including examination, diagnosis, and treatment. Based on the guidelines for the diagnosis and treatment of migraine, this study visualized the diagnosis and treatment process of integrative medicine for migraine through knowledge graph technology, which is of great significance for the clinical implementation of relevant guidelines for the diagnosis and treatment and the standardization of integrative medicine diagnosis and treatment for the dominant diseases in Chinese medicine.

Object Details

• Location: Deutsche Nationalbibliothek Frankfurt am Main
• Extent: Online-Ressource
• Language: Englisch

References and Relationships

• Bibliographic citation: Construction of Migraine Knowledge Graph Based on Diagnosis and Treatment Guidelines of Integrative Medicine ; volume:03 ; number:03 ; year:2023 ; pages:e91-e101 ; Chinese medicine and natural products ; 03, Heft 03 (2023), e91-e101

Contributors, Places and Time

• Contributor: Jiang, Yanhua ; Qiu, Shiqing ; Guo, Zhuang ; Ren, Huiling ; Li, Xiaoying ; Gao, Dongping ; Jing, Zhiwei ; Wang, Junwen

Further information

• DOI: 10.1055/s-0043-1774413
• URN: urn:nbn:de:101:1-2023113013160096679717
• Rights: Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
• Last update: 15.08.2025, 7:29 AM CEST