Key PointsQuestionCan machine learning improve the Pediatric Emergency Care Applied Research Network (PECARN) rules’ predictive accuracy to identify children at very low, intermediate, and high risk of clinically important traumatic brain injury? FindingsIn this cohort study of 42 412 children with head trauma, reanalysis of data from the PECARN group empirically suggests that novel machine-learning (optimal classification tree)–based rules perform as well as or better than the PECARN rules in identifying more children at very low risk of clinically important traumatic brain injury without missing more patients with clinically important traumatic brain injury. MeaningIf implemented in the electronic health record, the new rules may help reduce the number of unnecessary computed tomographic imaging scans, without missing more patients with clinically important traumatic brain injury than the PECARN rules.
JAMA Pediatrics – American Medical Association
Published: Jul 13, 2019
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