Predicting outcomes of acute kidney injury in critically ill patients using machine learning.

Nateghi Haredasht, Fateme; Viaene, Liesbeth; Pottel, Hans; De Corte, Wouter; Vens, Celine

Nateghi Haredasht, Fateme; Viaene, Liesbeth; Pottel, Hans; De Corte, Wouter; Vens, Celine.

Afiliación

Nateghi Haredasht F; KU Leuven, Campus KULAK - Department of Public Health and Primary Care, Etienne Sabbelaan 53, 8500, Kortrijk, Belgium. fateme.nateghi@kuleuven.be.
Viaene L; ITEC - imec and KU Leuven, Etienne Sabbelaan 51, 8500, Kortrijk, Belgium. fateme.nateghi@kuleuven.be.
Pottel H; Department of Nephrology, AZ Groeninge Hospital, President Kennedylaan 4, 8500, Kortrijk, Belgium.
De Corte W; KU Leuven, Campus KULAK - Department of Public Health and Primary Care, Etienne Sabbelaan 53, 8500, Kortrijk, Belgium.
Vens C; Department of Anesthesiology and Intensive Care Medicine, AZ Groeninge Hospital, President Kennedylaan 4, 8500, Kortrijk, Belgium.

Sci Rep ; 13(1): 9864, 2023 06 18.

Article en En | MEDLINE | ID: mdl-37331979

Asunto(s)

Lesión Renal Aguda; Insuficiencia Renal Crónica; Humanos; Enfermedad Crítica; Estudios Prospectivos; Lesión Renal Aguda/diagnóstico; Aprendizaje Automático

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Insuficiencia Renal Crónica / Lesión Renal Aguda Tipo de estudio: Diagnostic_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Sci Rep Año: 2023 Tipo del documento: Article

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