A machine learning model for the early diagnosis of bloodstream infection in patients admitted to the pediatric intensive care unit.

ABSTRACT: Bloodstream infection (BSI) is associated with increased morbidity and mortality in the pediatric intensive care unit (PICU) and high healthcare costs. Early detection and appropriate treatment of BSI may improve patient's outcome. Data on machine-learning models to predict BSI in pediatric patients are limited and neither study included time series data. We aimed to develop a machine learning model to predict an early diagnosis of BSI in patients admitted to the PICU. This was a retrospective cohort study of patients who had at least one positive blood culture result during stay at a PICU of a tertiary-care university hospital, from January 1st to December 31st 2019. Patients with positive blood culture results with growth of contaminants and those with incomplete data were excluded. Mode

SUBMITTER: Liporaci F 

PROVIDER: S-EPMC11062549 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature