A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients
- Location
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Deutsche Nationalbibliothek Frankfurt am Main
- ISSN
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1724-6059
- Extent
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Online-Ressource
- Language
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Englisch
- Notes
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online resource.
- Bibliographic citation
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A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients ; day:26 ; month:4 ; year:2021 ; pages:1-12
Journal of nephrology ; (26.4.2021), 1-12
- Creator
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Alfieri, Francesca
Ancona, Andrea
Tripepi, Giovanni
Crosetto, Dario
Randazzo, Vincenzo
Paviglianiti, Annunziata
Pasero, Eros
Vecchi, Luigi
Cauda, Valentina
Fagugli, Riccardo Maria
- Contributor
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SpringerLink (Online service)
- DOI
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10.1007/s40620-021-01046-6
- URN
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urn:nbn:de:101:1-2021090406022356569859
- Rights
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
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15.08.2025, 7:21 AM CEST
Data provider
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Associated
- Alfieri, Francesca
- Ancona, Andrea
- Tripepi, Giovanni
- Crosetto, Dario
- Randazzo, Vincenzo
- Paviglianiti, Annunziata
- Pasero, Eros
- Vecchi, Luigi
- Cauda, Valentina
- Fagugli, Riccardo Maria
- SpringerLink (Online service)
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Predictive value of the RIFLE urine output criteria on contrast-induced nephropathy in critically ill patients
Redefining urine output thresholds for acute kidney injury criteria in critically Ill patients: a derivation and validation study
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Incidence, timing and outcome of AKI in critically ill patients varies with the definition used and the addition of urine output criteria
Understanding urine output in critically ill patients
External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients
Concept for Continuously Tunable Output Filters for Digital Transmitter Architectures
Comparison of methods to normalize urine output in critically ill patients: a multicenter cohort study
Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs
Predictive value of the RIFLE urine output criteria on contrast-induced nephropathy in critically ill patients
Redefining urine output thresholds for acute kidney injury criteria in critically Ill patients: a derivation and validation study
A comparison of RIFLE with and without urine output criteria for acute kidney injury in critically ill patients
Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs
Urine neutrophil gelatinase-associated lipocalin and urine output as predictors of the successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury
Detection of low urine output by measuring urinary biomarkers
Incidence, timing and outcome of AKI in critically ill patients varies with the definition used and the addition of urine output criteria
Understanding urine output in critically ill patients
External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients
Concept for Continuously Tunable Output Filters for Digital Transmitter Architectures
Comparison of methods to normalize urine output in critically ill patients: a multicenter cohort study
Diagnostic decisions of specialist optometrists exposed to ambiguous deep-learning outputs
Predictive value of the RIFLE urine output criteria on contrast-induced nephropathy in critically ill patients
Redefining urine output thresholds for acute kidney injury criteria in critically Ill patients: a derivation and validation study
A comparison of RIFLE with and without urine output criteria for acute kidney injury in critically ill patients
Ready-to-use deep-learning surrogate models for problems with spatially variable inputs and outputs
Urine neutrophil gelatinase-associated lipocalin and urine output as predictors of the successful discontinuation of continuous renal replacement therapy in critically ill patients with acute kidney injury
Detection of low urine output by measuring urinary biomarkers