A Review on the Application of Knowledge Graph Technology in the Medical Field

Abstract

With the continuous development of Internet technology, knowledge graph construction has received increasing attention. Extracting useful medical knowledge from massive data is the key to analyzing big medical data. The knowledge graph is a semantic network that reveals relationships between entities. Medicine is one of the widely used fields of knowledge graphs, and the construction of a medical knowledge graph is also a research hotspot in artificial intelligence. Knowledge graph technology has broad application prospects in the field. First, this study comprehensively analyzes the structure and construction technology of the medical knowledge graph according to the characteristics of big data in the medical field, such as strong professionalism and complex structure. Second, this study summarizes the key technologies and research progress of the four modules of the medical knowledge graph: knowledge representation, knowledge extraction, knowledge fusion, and knowledge reasoning. Finally, with the major challenges and key problems of the current medical knowledge graph construction technology, its development prospects are prospects.

An Automatic Generation of Heterogeneous Knowledge Graph for Global Disease Support: A Demonstration of a Cancer Use Case

“…Knowledge graphs have proven to provide efficient and effective solutions to conceptualize a healthcare domain and thus be used for several healthcare systems [12]. Existing intelligent healthcare systems such as disease prediction and healthcare recommender systems lack a reliable knowledge base that has the power to represent heterogeneous data resources, having the ability to dynamically grow as more data is provided which could be visualized whenever needed [13][14][15][16][17][18]. The knowledge graph is an efficient representation of such a knowledge base.…”

Civil Aviation Travel Question and Answer Method Using Knowledge Graphs and Deep Learning

“…Knowledge graph technology has strong potential in deep Q&A, and its construction method has been a hot research topic. Qu [26] synthesized the structure and construction techniques of medical knowledge graphs according to the characteristics of big data in the medical field, such as strong specialization and complex structure, and reviewed the application of knowledge graph technology in the medical field. Chen et al [27] proposed a forest fire prediction method based on knowledge graphs and representation learning.…”

NetMe 2.0: a web-based platform for extracting and modeling knowledge from biomedical literature as a labeled graph

“…In particular, in the bio-medical area, thanks to fast-track publication journals, the number of published papers has increased significantly ( Ioannidis et al 2023 ), so identifying relevant knowledge from such sources is almost impossible for a human being. In this regard, computational methods for extracting knowledge representations are a suitable tool that has supported scientists in formulating novel hypotheses and deriving new conclusions (intelligent medicine) ( Qu 2022 ). As a result, network analysis over BioMedical Knowledge Graphs (BKG) has become a pivotal technology to uncover the fundamental biological processes underlying living organisms for precision medicine ( Wu et al 2023 ) and clinical decision support systems ( Caufield et al 2023 ); to identify new markers that indicate immune drug response in multiple cancer cohorts for immune therapies ( Tagliamento et al 2023 , Tan et al 2023 ); to reduce costs, time, and efficacy of clinical trials ( Chen et al 2022 ) and translational bio-medicine ( Bang et al 2023 ).…”