scholarly journals What factors are associated with the maintenance of PTSD after a motor vehicle accident? The role of sex differences in a help-seeking population

2006 ◽  
Vol 37 (3) ◽  
pp. 256-266 ◽  
Author(s):  
J. Gayle Beck ◽  
Sarah A. Palyo ◽  
Mark A. Canna ◽  
Edward B. Blanchard ◽  
Berglind Gudmundsdottir
Keyword(s):  
Sex Differences ◽  
Help Seeking ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident

Virtual reality and computer games in the treatment of driving phobia induced by a motor vehicle accident

2003 ◽  
Author(s):  
David Walshe ◽  
Elizabeth Lewis ◽  
Kathleen O'Sullivan ◽  
Brenda K. Wiederhold ◽  
Sun I. Kim
Keyword(s):  
Virtual Reality ◽  
Computer Games ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident

Machine Learning of Motor Vehicle Accident Categories from Narrative Data

1996 ◽  
Vol 35 (04/05) ◽  
pp. 309-316 ◽  
Author(s):  
M. R. Lehto ◽  
G. S. Sorock
Keyword(s):  
Machine Learning ◽  
Bayesian Model ◽  
Keyword Search ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Computer Search ◽  
Vehicle Accident ◽  
Learning Technique ◽  
Expert Ratings ◽  
Keyword Searches

Abstract:Bayesian inferencing as a machine learning technique was evaluated for identifying pre-crash activity and crash type from accident narratives describing 3,686 motor vehicle crashes. It was hypothesized that a Bayesian model could learn from a computer search for 63 keywords related to accident categories. Learning was described in terms of the ability to accurately classify previously unclassifiable narratives not containing the original keywords. When narratives contained keywords, the results obtained using both the Bayesian model and keyword search corresponded closely to expert ratings (P(detection)≥0.9, and P(false positive)≤0.05). For narratives not containing keywords, when the threshold used by the Bayesian model was varied between p>0.5 and p>0.9, the overall probability of detecting a category assigned by the expert varied between 67% and 12%. False positives correspondingly varied between 32% and 3%. These latter results demonstrated that the Bayesian system learned from the results of the keyword searches.

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