Journal of Data Science logo


Login Register

  1. Home
  2. Issues
  3. Volume 13, Issue 4 (2015)
  4. Improving Trauma Triage Models for Motor ...

Journal of Data Science

Submit your article Information
  • Article info
  • Related articles
  • More
    Article info Related articles

Improving Trauma Triage Models for Motor Vehicle Crashes using Event Data Recorders and Functional Data Analysis
Volume 13, Issue 4 (2015), pp. 637–662
Yaoyuan V. Tan   Carol A.C. Flannagan   Jonathan D. Rupp     All authors (4)

Authors

 
Placeholder
https://doi.org/10.6339/JDS.201510_13(4).0002
Pub. online: 4 August 2022      Type: Research Article      Open accessOpen Access

Published
4 August 2022

Abstract

Abstract: Quick identification of severe injury crashes can help Emergency Medical Services (EMS) better allocate their scarce resources to improve the survival of severely injured crash victims by providing them with a fast and timely response. Data broadcast from a vehicle’s Event Data Recorder (EDR) provide an opportunity to capture crash information and send them to EMS near real-time. A key feature of EDR data is a longitudinal measure of crash deceleration. We used functional data analysis (FDA) to ascertain key features of the deceleration trajectories (absolute integral, absolute in- tegral of its slope, and residual variance) to develop and verify a risk predic- tion model for serious (AIS 3+) injuries. We used data from the 2002-2012 EDR reports and the National Highway and National Automotive Sampling System (NASS) Crashworthiness Data System (CDS) datasets available on the National Transportation Safety Administration (NHTSA) website. We consider a variety of approaches to model deceleration data, including non- penalized and penalized splines and a variable selection method, ultimately obtaining a model with a weighted AUC of 0.93. A novel feature of our approach is the use of residual variance as a measure of predictive risk. Our model can be viewed as an important first step towards developing a real- time prediction model capable of predicting the risk of severe injury in any motor vehicle crash.

Related articles PDF XML
Related articles PDF XML

Copyright
No copyright data available.

Keywords
Clinical prediction modeling cross-validation motor vehicle crash injuries

Metrics
since February 2021
746

Article info
views

425

PDF
downloads

Export citation

Copy and paste formatted citation
Placeholder

Download citation in file


Share


RSS

Journal of data science

  • Online ISSN: 1683-8602
  • Print ISSN: 1680-743X

About

  • About journal

For contributors

  • Submit
  • OA Policy
  • Become a Peer-reviewer

Contact us

  • JDS@ruc.edu.cn
  • No. 59 Zhongguancun Street, Haidian District Beijing, 100872, P.R. China
Powered by PubliMill  •  Privacy policy