Classification tree modeling to identify severe and moderate vehicular injuries in young and middle-aged adults

作者:

Highlights:

摘要

ObjectivesMotor vehicle crashes are a leading cause of mortality and morbidity worldwide. Even though trauma centers provide the gold standard of care for motor vehicle crash patients with life- or limb-threatening injuries, many whose lives might be saved by trauma center care are treated instead at non-trauma center hospitals. Triage algorithms, designed to identify patients with life- or limb-threatening injuries who should be transported to a trauma center, lack appropriate sensitivity to many of these injuries. The challenge to the trauma community is differentiating patients with life- or limb-threatening injuries from those with less severe injuries at the crash scene so that the patients can be transported to the most appropriate level of care. The purpose of this study was to use crash scene data available to emergency responders to classify adults with moderate and severe injuries. These classifiers might be useful to guide triage decision making.

论文关键词:Classification and regression tree analysis,Artificial intelligence,Decision support techniques,Data mining,Triage,Emergency Medical Services,Injury Severity Score

论文评审过程:Received 1 February 2008, Revised 31 October 2008, Accepted 3 November 2008, Available online 16 December 2008.

论文官网地址:https://doi.org/10.1016/j.artmed.2008.11.002