Medical Knowledge Graph: Data Sources, Construction, Reasoning, and Applications

• Published in: Big Data Mining and Analytics Vol. 6; no. 2; pp. 201 - 217
• Main Authors: Wu, Xuehong, Duan, Junwen, Pan, Yi, Li, Min
• Format: Journal Article
• Language: English
• Published: Beijing Tsinghua University Press 01.06.2023
• Subjects: Artificial intelligence; Big Data; Clinical trials; Computer science; Data mining; Disease; Electronic health records; Information sources; intelligent healthcare; intelligent medical applications; Knowledge; knowledge graph construction; knowledge reasoning; Knowledge representation; Medical diagnosis; medical knowledge graph; Medical Subject Headings-MeSH; Ontology; Reasoning; Semantics; Textbooks
• ISSN: 2096-0654; 2097-406X
• DOI: 10.26599/BDMA.2022.9020021

Medical knowledge graphs (MKGs) are the basis for intelligent health care, and they have been in use in a variety of intelligent medical applications. Thus, understanding the research and application development of MKGs will be crucial for future relevant research in the biomedical field. To this end, we offer an in-depth review of MKG in this work. Our research begins with the examination of four types of medical information sources, knowledge graph creation methodologies, and six major themes for MKG development. Furthermore, three popular models of reasoning from the viewpoint of knowledge reasoning are discussed. A reasoning implementation path (RIP) is proposed as a means of expressing the reasoning procedures for MKG. In addition, we explore intelligent medical applications based on RIP and MKG and classify them into nine major types. Finally, we summarize the current state of MKG research based on more than 130 publications and future challenges and opportunities.