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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主要な著者:	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
フォーマット:	Artigo
言語:	Inglês
出版事項:	BMC 2024-04-01
シリーズ:	BMC Nephrology
主題:	Renal insufficiency Renal replacement therapy Prediction Artificial intelligence Machine learning
オンライン･アクセス:	https://doi.org/10.1186/s12882-024-03545-7
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