Machine Learning Using Electroencephalography Predicts Acute Cerebral Injury in the Pediatric ICU.

Arnold J Sansevere^1,2,3,4, Syed Muhammad Anwar^5,6, Julia S Keenan^7,8, Caroline R Conley⁹, Katelyn Staso⁹, Dana B Harrar^7,8,10

¹Division of Epilepsy and Neurophysiology, Children's National Hospital, Washington, DC, USA. Arnold.sansevere@tuftsmedicalcenter.org.

²Department of Neurology, Children's National Hospital, Washington, DC, USA. Arnold.sansevere@tuftsmedicalcenter.org.

³Departments of Neurology and Pediatrics, The George Washington University School of Medicine, Washington, DC, USA. Arnold.sansevere@tuftsmedicalcenter.org.

⁴Department of Pediatrics/Division of Pediatric Neurology, Tufts Medical Center, Tufts University of Medicine, 800 Washington Street, Boston, MA, 02111, USA. Arnold.sansevere@tuftsmedicalcenter.org.

⁵School of Medicine and Health Sciences, George Washington University, Washington, DC, USA.

⁶Sheikh Zayed Institute for Pediatric Surgical Innovation, Children's National Hospital, Washington, DC, USA.

⁷Division of Epilepsy and Neurophysiology, Children's National Hospital, Washington, DC, USA.

⁸Department of Neurology, Children's National Hospital, Washington, DC, USA.

⁹Division of Critical Care Medicine, Children's National Hospital, Washington, DC, USA.

¹⁰Departments of Neurology and Pediatrics, The George Washington University School of Medicine, Washington, DC, USA.

PubMed DOI

Abstract

BACKGROUND AND OBJECTIVES: The objective of this study was to apply and compare machine learning techniques using clinical and electroencephalography (EEG) background features to predict acute cerebral injury in the pediatric intensive care unit (PICU). METHODS: This was a prospective study of patients admitted to the PICU from July 2021 to January 2023. We excluded patients with preexisting cere…

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