| Literature DB >> 30485240 |
Andrew T Hale1,2, David P Stonko2,3, Jaims Lim2, Oscar D Guillamondegui2,3,4, Chevis N Shannon2,4,5, Mayur B Patel2,6,7,4.
Abstract
In BriefPediatric traumatic brain injury (TBI) is common, but not all injuries require hospitalization. A computational tool for ruling-in patients who will have clinically relevant TBI (CRTBI) would be valuable, providing an evidence-based mechanism for safe discharge. Here, using data from 12,902 patients from the Pediatric Emergency Care Applied Research Network (PECARN) TBI data set, the authors utilize artificial intelligence to predict CRTBI using radiologist-interpreted CT information with > 99% sensitivity and an AUC of 0.99.Entities:
Keywords: ANN = artificial neural network; AUC = area under the curve; CRTBI = clinically relevant TBI; EMR = electronic medical record; NPV = negative predictive value; PECARN = Pediatric Emergency Care Applied Research Network; ROC = receiver operator characteristic; TBI; TBI = traumatic brain injury; artificial intelligence; machine learning; pediatrics; trauma
Mesh：
Year: 2018 PMID： 30485240 PMCID： PMC9549179 DOI： 10.3171/2018.8.PEDS18370
Source DB: PubMed Journal: J Neurosurg Pediatr ISSN： 1933-0707 Impact factor: 2.713