Prediction of Acute Kidney Injury in the Intensive Care Unit: Preliminary Findings in a European Open Access Database.
Acute Kidney Injury
AmsterdamUMCdb
ICU
Predictive Modeling
Journal
Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582
Informations de publication
Date de publication:
25 May 2022
25 May 2022
Historique:
entrez:
25
5
2022
pubmed:
26
5
2022
medline:
27
5
2022
Statut:
ppublish
Résumé
Acute kidney injury (AKI) is a common complication in critically ill patients and is associated with long-term complications and an increased mortality. This work presents preliminary findings from the first freely available European intensive care database released by Amsterdam UMC. A machine learning (ML) model was developed to predict AKI in the intensive care unit 12 hours before the actual event. Main features of the model included medications and hemodynamic parameters. Our models perform with an accuracy of 81.8% on moderate to severe AKI and 79.8% on all AKI patients. Those results can compete with models reported in the literature and introduce an ML model for AKI based on European patient data.
Identifiants
pubmed: 35612039
pii: SHTI220419
doi: 10.3233/SHTI220419
doi:
Types de publication
Journal Article
Langues
eng