Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12874-015-0015-0
Title: Predictive modeling in pediatric traumatic brain injury using machine learning Data analysis, statistics and modelling
Authors: Chong, S.-L
Liu, N 
Barbier, S
Ong, M.E.H 
Keywords: algorithm
Brain Injuries
case control study
child
complication
factual database
female
health survey
human
injury scale
machine learning
male
multivariate analysis
preschool child
receiver operating characteristic
retrospective study
skull fracture
statistical model
statistics and numerical data
theoretical model
traffic accident
unconsciousness
vomiting
x-ray computed tomography
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
Issue Date: 2015
Publisher: BioMed Central Ltd.
Citation: Chong, S.-L, Liu, N, Barbier, S, Ong, M.E.H (2015). Predictive modeling in pediatric traumatic brain injury using machine learning Data analysis, statistics and modelling. BMC Medical Research Methodology 15 (1) : 22. ScholarBank@NUS Repository. https://doi.org/10.1186/s12874-015-0015-0
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?
Source Title: BMC Medical Research Methodology
URI: https://scholarbank.nus.edu.sg/handle/10635/174294
ISSN: 14712288
DOI: 10.1186/s12874-015-0015-0
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