Predicting chronic kidney disease progression with artificial intelligence

• Published in: BMC nephrology Vol. 25; no. 1; p. 148
• Main Authors: Isaza-Ruget, Mario A, Yomayusa, Nancy, González, Camilo A, H, Catherine Alvarado, de Oro V, Fabio A, Cely, Andrés, Murcia, Jossie, Gonzalez-Velez, Abel, Robayo, Adriana, Colmenares-Mejía, Claudia C, Castillo, Andrea, Conde, María I
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 26.04.2024; BioMed Central; BMC
• Subjects: Adult; Aged; Algorithms; Artificial Intelligence; Cardiovascular disease; Care and treatment; Cholesterol; Chronic kidney failure; Cohort Studies; Creatinine; Development and progression; Diabetes; Disease Progression; Epidermal growth factor receptors; Female; Glomerular Filtration Rate; Health aspects; Hemodialysis; Hemoglobin; High density lipoprotein; Humans; Hypertension; Kidney diseases; Kidney transplants; Learning algorithms; Low density lipoprotein; Machine Learning; Male; Methods; Middle Aged; Neural networks; Prediction; Prediction models; Regression analysis; Renal insufficiency; Renal Insufficiency, Chronic - physiopathology; Renal Insufficiency, Chronic - therapy; Renal Replacement Therapy; Retrospective Studies; Tool use; Urine; Colombia; Renal insufficiency; Renal replacement therapy; Artificial intelligence; Machine learning; Prediction

The use of tools that allow estimation of the probability of progression of chronic kidney disease (CKD) to advanced stages has not yet achieved significant practical importance in clinical setting. This study aimed to develop and validate a machine learning-based model for predicting the need for r...