George Bahouth - Academia.edu (original) (raw)
Uploads
Papers by George Bahouth
Annual proceedings / Association for the Advancement of Automotive Medicine. Association for the Advancement of Automotive Medicine, 2003
The advent of Automatic Crash Notification Systems (ACN) offers the possibility of immediately lo... more The advent of Automatic Crash Notification Systems (ACN) offers the possibility of immediately locating crashes and of determining the crash characteristics by analyzing the data transmitted from the vehicle. A challenge to EMS decision makers is to identify those crashes with serious injuries and deploy the appropriate rescue and treatment capabilities. The objective of this paper is to determine the crash characteristics that increase the risk of serious injury. Within this paper, regression models are presented which relate occupant, vehicle and impact characteristics to the probability of serious injury using the Maximum Abbreviated Injury Scale Level (MAIS). The accuracy of proposed models were evaluated using National Automotive Sampling System/ Crashworthiness Data System (NASS/CDS) and Crash Injury Research and Engineering Network (CIREN) case data. Cumulatively, the positive prediction rate of models identifying the likelihood of MAIS3 and higher injuries was 74.2%. Crash m...
Annual proceedings / Association for the Advancement of Automotive Medicine. Association for the Advancement of Automotive Medicine, 2003
The advent of Automatic Crash Notification Systems (ACN) offers the possibility of immediately lo... more The advent of Automatic Crash Notification Systems (ACN) offers the possibility of immediately locating crashes and of determining the crash characteristics by analyzing the data transmitted from the vehicle. A challenge to EMS decision makers is to identify those crashes with serious injuries and deploy the appropriate rescue and treatment capabilities. The objective of this paper is to determine the crash characteristics that increase the risk of serious injury. Within this paper, regression models are presented which relate occupant, vehicle and impact characteristics to the probability of serious injury using the Maximum Abbreviated Injury Scale Level (MAIS). The accuracy of proposed models were evaluated using National Automotive Sampling System/ Crashworthiness Data System (NASS/CDS) and Crash Injury Research and Engineering Network (CIREN) case data. Cumulatively, the positive prediction rate of models identifying the likelihood of MAIS3 and higher injuries was 74.2%. Crash m...