Predicting chronic kidney disease progression with artificial intelligence

Abstract Background 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 predi...

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Bibliografski detalji

Glavni autori:	Mario A. Isaza-Ruget, Nancy Yomayusa, Camilo A. González, Catherine Alvarado H., Fabio A. de Oro V., Andrés Cely, Jossie Murcia, Abel Gonzalez-Velez, Adriana Robayo, Claudia C. Colmenares-Mejía, Andrea Castillo, María I. Conde
Format:	Artigo
Jezik:	Inglês
Izdano:	BMC 2024-04-01
Serija:	BMC Nephrology
Teme:	Renal insufficiency Renal replacement therapy Prediction Artificial intelligence Machine learning
Online pristup:	https://doi.org/10.1186/s12882-024-03545-7
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