Development and validation of an interpretable predictive machine learning model for successful weaning of continuous renal replacement therapy

Abstract Continuous renal replacement therapy (CRRT) is a vital intervention for critically ill patients with severe acute kidney injury, yet no standardized criteria exist to determine the optimal time for its discontinuation. We developed and validated machine learning models to predict successful...

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Autors principals:	Benjamin Popoff, Boris Delange, Jonathan Nicolas, Arthur Le Gall, Badisse Dahamna, Marc Cuggia, Thomas Clavier, Guillaume Bouzillé
Format:	Artigo
Idioma:	Inglês
Publicat:	Nature Portfolio 2025-11-01
Col·lecció:	Scientific Reports
Matèries:	Renal replacement therapy Acute kidney injury Machine learning Critical care Clinical decision support system
Accés en línia:	https://doi.org/10.1038/s41598-025-26774-8
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