Abstract
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 language | English (US) |
|---|---|
| Title of host publication | Annals of Advances in Automotive Medicine |
| Pages | 223-230 |
| Number of pages | 8 |
| Volume | 56 |
| State | Published - 2012 |
| Event | 56th Annual Scientific Conference of the Association for the Advancement of Automotive Medicine - Seattle, WA, United States Duration: Oct 14 2012 → Oct 17 2012 |
Other
| Other | 56th Annual Scientific Conference of the Association for the Advancement of Automotive Medicine |
|---|---|
| Country | United States |
| City | Seattle, WA |
| Period | 10/14/12 → 10/17/12 |
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ASJC Scopus subject areas
- Automotive Engineering
- Biomedical Engineering
- Safety, Risk, Reliability and Quality
- Medicine(all)
Cite this
Influence of injury risk thresholds on the performance of an algorithm to predict crashes with serious injuries. / Bahouth, George; Digges, Kennerly; Schulman, Carl I.
Annals of Advances in Automotive Medicine. Vol. 56 2012. p. 223-230.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Influence of injury risk thresholds on the performance of an algorithm to predict crashes with serious injuries
AU - Bahouth, George
AU - Digges, Kennerly
AU - Schulman, Carl I
PY - 2012
Y1 - 2012
N2 - 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.
AB - 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.
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M3 - Conference contribution
VL - 56
SP - 223
EP - 230
BT - Annals of Advances in Automotive Medicine
ER -