Methods The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the "AmsterdamUMC database") admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients.

External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients

Tripepi, Giovanni;Politi, Cristina;
2022

Abstract

Methods The independent cohort was composed of 10'596 patients from the university hospital ICU of Amsterdam (the "AmsterdamUMC database") admitted to their intensive care units. In this cohort, we analysed the accuracy of algorithms based on logistic regression and deep learning methods. The accuracy of investigated algorithms had previously been tested with electronic intensive care unit (eICU) and MIMIC-III patients.
2022
Istituto di Fisiologia Clinica - IFC - Sede Secondaria di Reggio Calabria
Inglese
35
8
2047
2056
10
https://link.springer.com/article/10.1007/s40620-022-01335-8
Esperti anonimi
Acute kidney injury
Artificial intelligence
eAlert
KDIGO
Internazionale
Stampa
10
info:eu-repo/semantics/article
262
Alfieri, Francesca; Ancona, Andrea; Tripepi, Giovanni; Randazzo, Vincenzo; Paviglianiti, Annunziata; Pasero, Eros; Vecchi, Luigi; Politi, Cristina; Ca...espandi
01 Contributo su Rivista::01.01 Articolo in rivista
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/417763
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