Influence of injury risk thresholds on the performance of an algorithm to predict crashes with serious injuries

George Bahouth, Kennerly Digges, Carl Schulman

Research output: Contribution to journalConference article

21 Scopus citations


This paper presents methods to estimate crash injury risk based on crash characteristics captured by some passenger vehicles equipped with Advanced Automatic Crash Notification technology. The resulting injury risk estimates could be used within an algorithm to optimize rescue care. Regression analysis was applied to the National Automotive Sampling System / Crashworthiness Data System (NASS/CDS) to determine how variations in a specific injury risk threshold would influence the accuracy of predicting crashes with serious injuries. The recommended thresholds for classifying crashes with severe injuries are 0.10 for frontal crashes and 0.05 for side crashes. The regression analysis of NASS/CDS indicates that these thresholds will provide sensitivity above 0.67 while maintaining a positive predictive value in the range of 0.20.

Original languageEnglish (US)
Pages (from-to)223-230
Number of pages8
JournalAnnals of Advances in Automotive Medicine
StatePublished - Dec 1 2012
Event56th Annual Scientific Conference of the Association for the Advancement of Automotive Medicine - Seattle, WA, United States
Duration: Oct 14 2012Oct 17 2012


ASJC Scopus subject areas

  • Automotive Engineering
  • Biomedical Engineering
  • Safety, Risk, Reliability and Quality
  • Medicine(all)

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