Predictive modeling in pediatric traumatic brain injury using machine learning

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...

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Bibliographic Details
Published in:BMC medical research methodology 2015-03, Vol.15 (1), p.22-22, Article 22
Main Authors: Chong, Shu-Ling, Liu, Nan, Barbier, Sylvaine, Ong, Marcus Eng Hock
Format: Article
Language:English
Subjects:
Accidents, Traffic
Algorithms
Analysis
Brain Injuries - diagnosis
Brain Injuries - etiology
Case-Control Studies
Child
Child, Preschool
CT imaging
Databases, Factual - statistics & numerical data
Female
Humans
Injury Severity Score
Logistic Models
Machine Learning
Male
Medical research
Medicine, Experimental
Models, Theoretical
Multivariate Analysis
Population Surveillance
Retrospective Studies
ROC Curve
Skull Fractures - complications
Tomography, X-Ray Computed
Unconsciousness - complications
Vomiting - complications
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Summary: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 
ISSN:1471-2288
1471-2288
DOI:10.1186/s12874-015-0015-0