Cargando…
External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients
OBJECTIVES: The purpose of this study was to externally validate algorithms (previously developed and trained in two United States populations) aimed at early detection of severe oliguric AKI (stage 2/3 KDIGO) in intensive care units patients. METHODS: The independent cohort was composed of 10'...
Autores principales: | Alfieri, Francesca, Ancona, Andrea, Tripepi, Giovanni, Randazzo, Vincenzo, Paviglianiti, Annunziata, Pasero, Eros, Vecchi, Luigi, Politi, Cristina, Cauda, Valentina, Fagugli, Riccardo Maria |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer International Publishing
2022
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9585008/ https://www.ncbi.nlm.nih.gov/pubmed/35554875 http://dx.doi.org/10.1007/s40620-022-01335-8 |
Ejemplares similares
-
A deep-learning model to continuously predict severe acute kidney injury based on urine output changes in critically ill patients
por: Alfieri, Francesca, et al.
Publicado: (2021) -
Continuous and early prediction of future moderate and severe Acute Kidney Injury in critically ill patients: Development and multi-centric, multi-national external validation of a machine-learning model
por: Alfieri, Francesca, et al.
Publicado: (2023) -
A Comparison of Deep Learning Techniques for Arterial Blood Pressure Prediction
por: Paviglianiti, Annunziata, et al.
Publicado: (2021) -
Development and Validation of an Algorithm for the Digitization of ECG Paper Images
por: Randazzo, Vincenzo, et al.
Publicado: (2022) -
Outcome in noncritically ill patients with acute kidney injury requiring dialysis: Effects of differing medical staffs and organizations
por: Fagugli, Riccardo Maria, et al.
Publicado: (2016)