Machine Learning Models for Predicting the Progression of Diabetic Kidney Disease

Le résumé fourni par la source

This scoping review aims to systematically map and summarize the existing evidence on machine learning (ML) models developed to predict the progression of diabetic kidney disease (DKD). The review will examine the types of ML algorithms used, predictor variables incorporated, renal outcomes predicted, validation approaches, model performance metrics, and clinical applicability. Studies involving adults with diabetes and DKD will be identified through comprehensive searches of major biomedical and interdisciplinary databases. Following Joanna Briggs Institute (JBI) methodology and PRISMA-ScR reporting guidelines, the review will identify methodological trends, evidence gaps, and opportunities for future research to support the development of clinically relevant predictive models in DKD.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.