Predictive modeling in pediatric traumatic brain injury using machine learning.
Journal:
BMC medical research methodology
Published Date:
Mar 17, 2015
Abstract
BACKGROUND: Pediatric traumatic brain injury (TBI) constitutes a significant burden and diagnostic challenge in the emergency department (ED). While large North American research networks have derived clinical prediction rules for the head injured child, these may not be generalizable to practices in countries with traditionally low rates of computed tomography (CT). We aim to study predictors for moderate to severe TBI in our ED population aged < 16 years.
Authors
Keywords
Accidents, Traffic
Algorithms
Brain Injuries
Case-Control Studies
Child
Child, Preschool
Databases, Factual
Female
Humans
Injury Severity Score
Logistic Models
Machine Learning
Male
Models, Theoretical
Multivariate Analysis
Population Surveillance
Retrospective Studies
ROC Curve
Skull Fractures
Tomography, X-Ray Computed
Unconsciousness
Vomiting