Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.

Journal: Journal of the American Medical Informatics Association : JAMIA

PMID: 30295770

Abstract

OBJECTIVE: Quantify physiologically acceptable PICU-discharge vital signs and develop machine learning models to predict these values for individual patients throughout their PICU episode.

Authors

Cameron S Carlin

Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Long V Ho

The Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit Children's Hospital Los Angeles, Los Angeles, CA.
David R Ledbetter
Melissa D Aczon

Laura P. and Leland K. Whittier Virtual Pediatric Intensive Care Unit, Children's Hospital Los Angeles, Los Angeles, CA, USA.
Randall C Wetzel

Keywords

Adolescent Child Child, Preschool Humans Infant Intensive Care Units, Pediatric Machine Learning Neural Networks, Computer Patient Discharge Reference Values Regression Analysis Vital Signs

External Resources

View on PubMed Access via DOI PubMed (30295770)

Predicting individual physiologically acceptable states at discharge from a pediatric intensive care unit.

Abstract

Authors

Keywords

External Resources

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