Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules

Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care... 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png JAMA Pediatrics American Medical Association

Comparison of Machine Learning Optimal Classification Trees With the Pediatric Emergency Care Applied Research Network Head Trauma Decision Rules

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Publisher
American Medical Association
Copyright
Copyright 2019 American Medical Association. All Rights Reserved.
ISSN
2168-6203
eISSN
2168-6211
DOI
10.1001/jamapediatrics.2019.1068
Publisher site
See Article on Publisher Site

Abstract

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.

Journal

JAMA PediatricsAmerican Medical Association

Published: Jul 13, 2019

References

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