Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.

Journal: Pediatric critical care medicine : a journal of the Society of Critical Care Medicine and the World Federation of Pediatric Intensive and Critical Care Societies

PMID: 39982152

Abstract

OBJECTIVES: To describe the deployment of pediatric Calculated Assessment of Risk and Triage (pCART), a machine learning (ML) model to predict the risk of the direct ward to the ICU transfer within 12 hours, and the associated improved outcomes among hospitalized children.

Authors

Anoop Mayampurath

Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States.
Kyle Carey

Department of Medicine, University of Chicago, Chicago, IL.
Brett Palama

Department of Pediatrics, University of Chicago, Chicago, IL.
Monica Gonzalez

Department of Pediatrics, University of Chicago, Chicago, IL.
Joe Reid

Department of Medicine, University of Chicago, Chicago, IL.
Allison H Bartlett

Department of Pediatrics, University of Chicago, Chicago, IL.
Matthew Churpek

Department of Medicine, University of Chicago, Chicago, IL, United States of America.
Dana Edelson

Department of Medicine, University of Chicago, Chicago IL, United States.
Priti Jani

Department of Pediatrics, University of Chicago, Chicago, IL.

Keywords

Adolescent Child Child, Preschool Early Warning Score Female Hospital Mortality Humans Infant Infant, Newborn Intensive Care Units, Pediatric Machine Learning Male Patient Transfer Respiration, Artificial Risk Assessment Triage

External Resources

View on PubMed Access via DOI PubMed (39982152)

Machine Learning-Based Pediatric Early Warning Score: Patient Outcomes in a Pre- Versus Post-Implementation Study, 2019-2023.

Abstract

Authors

Keywords

External Resources

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