Long-term prediction models for vision-threatening diabetic retinopathy using medical features from data warehouse

• Published in: Scientific Reports Vol. 12; no. 1; pp. 8476 - 8
• Main Authors: Jo, Kwanhoon, Chang, Dong Jin, Min, Ji Won, Yoo, Young-Sik, Lyu, Byul, Kwon, Jin Woo, Baek, Jiwon
• Format: Journal Article
• Language: English
• Published: London Springer Science and Business Media LLC 19.05.2022; Nature Publishing Group UK; Nature Publishing Group; Nature Portfolio
• Subjects: 692/163/2743/137; 692/499; 692/699/2743/137; 692/699/3161/3175; 692/700; Accuracy; Blood pressure; Creatinine; Data warehouses; Data Warehousing; Diabetes; Diabetes mellitus (non-insulin dependent); Diabetes Mellitus, Type 2; Diabetic Retinopathy; Electronic medical records; Glomerular filtration rate; Glycated Hemoglobin; Hemoglobin; Humanities and Social Sciences; Humans; Learning algorithms; Machine learning; Medical records; Medicine; multidisciplinary; Patients; Performance evaluation; Prediction models; Primary care; Q; R; Retinopathy; Risk Factors; ROC Curve; Science; Science (multidisciplinary); Vision

We sought to evaluate the performance of machine learning prediction models for identifying vision-threatening diabetic retinopathy (VTDR) in patients with type 2 diabetes mellitus using only medical data from data warehouse. This is a multicenter electronic medical records review study. Patients wi...