Random Forest Prognostication of Survival and 6-Month Outcome in Pediatric Patients Following Decompressive Craniectomy for Traumatic Brain Injury.

Journal: World neurosurgery

PMID: 39476933

Abstract

BACKGROUND: There is a dearth of literature regarding prognostic and predictive factors for outcome following pediatric decompressive craniectomy (DC) performed after traumatic brain injury (TBI). The aim of this study was to develop a random forest machine learning algorithm to predict outcomes following DC in pediatric patients.

Authors

Ryan D Morgan

School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA. Electronic address: ryan.dean.morgan@ttuhsc.edu.
Brandon W Youssi

School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.
Rafael Cacao

Division of Neurosurgery, Department of Pediatrics, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.
Cristian Hernandez

School of Medicine, Texas Tech University Health Sciences Center, Lubbock, Texas, USA.
Laszlo Nagy

Industrial, Manufacturing and Systems Engineering Department, Texas Tech University, Lubbock, Texas, USA.

Keywords

Adolescent Brain Injuries, Traumatic Child Child, Preschool Decompressive Craniectomy Female Glasgow Outcome Scale Hospital Mortality Humans Infant Machine Learning Male Prognosis Random Forest Retrospective Studies Treatment Outcome

External Resources

View on PubMed Access via DOI PubMed (39476933)

Random Forest Prognostication of Survival and 6-Month Outcome in Pediatric Patients Following Decompressive Craniectomy for Traumatic Brain Injury.

Abstract

Authors

Keywords

External Resources

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