Cargando…
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
BACKGROUND: Acute Kidney Injury (AKI) is a major complication in patients admitted to Intensive Care Units (ICU), causing both clinical and economic burden on the healthcare system. This study develops a novel machine-learning (ML) model to predict, with several hours in advance, the AKI episodes of...
Autores principales: | Alfieri, Francesca, Ancona, Andrea, Tripepi, Giovanni, Rubeis, Andrea, Arjoldi, Niccolò, Finazzi, Stefano, Cauda, Valentina, Fagugli, Riccardo Maria |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10368244/ https://www.ncbi.nlm.nih.gov/pubmed/37490482 http://dx.doi.org/10.1371/journal.pone.0287398 |
Ejemplares similares
-
External validation of a deep-learning model to predict severe acute kidney injury based on urine output changes in critically ill patients
por: Alfieri, Francesca, et al.
Publicado: (2022) -
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) -
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) -
Multi-Focal, Multi-Centric Angiosarcoma of Bone
por: Kakouri, Eleni, et al.
Publicado: (1997) -
Novel Harmonization Method for Multi-Centric Radiomic Studies in Non-Small Cell Lung Cancer
por: Bertolini, Marco, et al.
Publicado: (2022)