Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

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

Published Date: Oct 1, 2018

Abstract

OBJECTIVES: We used artificial intelligence to develop a novel algorithm using physiomarkers to predict the onset of severe sepsis in critically ill children.

Authors

Rishikesan Kamaleswaran

Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, GA.
Oguz Akbilgic

1Department of Pediatrics, University of Tennessee Health Science Center - Oak Ridge National Laboratory- (UTHSC-ORNL), Center for Biomedical Informatics, Memphis, TN USA.
Madhura A Hallman

Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
Alina N West

Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
Robert L Davis

Center for Biomedical Informatics, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.
Samir H Shah

Division of Pediatric Critical Care Medicine, Department of Pediatrics, University of Tennessee Health Science Center, Memphis, TN.

Keywords

Adolescent Artificial Intelligence Case-Control Studies Child Female Heart Rate Humans Intensive Care Units, Pediatric Logistic Models Machine Learning Male Monitoring, Physiologic Organ Dysfunction Scores Predictive Value of Tests Prospective Studies Respiratory Rate Sepsis

External Resources

View on PubMed Access via DOI PubMed (30052552)

Applying Artificial Intelligence to Identify Physiomarkers Predicting Severe Sepsis in the PICU.

Abstract

Authors

Keywords

External Resources

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