Prediction of Diabetic Macular Edema Using Knowledge Graph

• Published in: Diagnostics (Basel) Vol. 13; no. 11; p. 1858
• Main Authors: Li, Zhi-Qing, Fu, Zi-Xuan, Li, Wen-Jun, Fan, Hao, Li, Shu-Nan, Wang, Xi-Mo, Zhou, Peng
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26.05.2023; MDPI
• Subjects: Analysis; Artificial intelligence; clinical decision support system; Data mining; Decision making; Diabetes therapy; diabetic macular edema; disease prediction; Dropsy; Edema; knowledge graph; Machine learning; Medical research; Medicine, Experimental; neo4j; Ontology; personalized prediction; Recommender systems; Risk factors; China; clinical decision support system; neo4j; disease prediction; knowledge graph; diabetic macular edema; personalized prediction
• ISSN: 2075-4418; 2075-4418
• DOI: 10.3390/diagnostics13111858

Diabetic macular edema (DME) is a significant complication of diabetes that impacts the eye and is a primary contributor to vision loss in individuals with diabetes. Early control of the related risk factors is crucial to reduce the incidence of DME. Artificial intelligence (AI) clinical decision-making tools can construct disease prediction models to aid in the clinical screening of the high-risk population for early disease intervention. However, conventional machine learning and data mining techniques have limitations in predicting diseases when dealing with missing feature values. To solve this problem, a knowledge graph displays the connection relationships of multi-source and multi-domain data in the form of a semantic network to enable cross-domain modeling and queries. This approach can facilitate the personalized prediction of diseases using any number of known feature data. In this study, we proposed an improved correlation enhancement algorithm based on knowledge graph reasoning to comprehensively evaluate the factors that influence DME to achieve disease prediction. We constructed a knowledge graph based on Neo4j by preprocessing the collected clinical data and analyzing the statistical rules. Based on reasoning using the statistical rules of the knowledge graph, we used the correlation enhancement coefficient and generalized closeness degree method to enhance the model. Meanwhile, we analyzed and verified these models' results using link prediction evaluation indicators. The disease prediction model proposed in this study achieved a precision rate of 86.21%, which is more accurate and efficient in predicting DME. Furthermore, the clinical decision support system developed using this model can facilitate personalized disease risk prediction, making it convenient for the clinical screening of a high-risk population and early disease intervention.