Demographic and clinical characteristics of motor vehicle accident victims in the community general health outpatient clinic: a comparison of PTSD and non-PTSD subjects

2007 ◽  
Vol 24 (4) ◽  
pp. 244-250 ◽  
Author(s):  
Marina Kupchik ◽  
Rael D. Strous ◽  
Rina Erez ◽  
Noach Gonen ◽  
Abraham Weizman ◽  
...  
Keyword(s):  
Outpatient Clinic ◽  
General Health ◽  
Clinical Characteristics ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident

scholarly journals Clinical Characteristics of Patients with Dizziness after Motor Vehicle Accident

2017 ◽  
Vol 13 (3) ◽  
pp. 374-378 ◽  
Author(s):  
Min Bum Kim ◽  
Suk Won Chang ◽  
Gil Chai Lim ◽  
Chan Il Song
Keyword(s):  
Clinical Characteristics ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident

Clinical Characteristics of Dizziness after Motor Vehicle Accident

2009 ◽  
Vol 52 (9) ◽  
pp. 718 ◽  
Author(s):  
Min-Young Lee ◽  
Sung-Won Chung ◽  
Myung-Whan Suh ◽  
Chung-Ku Rhee ◽  
Jae Yun Jung
Keyword(s):  
Clinical Characteristics ◽  
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.

Aspirated teeth - rigid or flexible bronchoscopy

2019 ◽  
pp. 1-2
Keyword(s):  
Clinical Practice ◽  
Foreign Bodies ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Flexible Bronchoscopy ◽  
Vehicle Accident

Tracheobronchial foreign bodies are a common problem in clinical practice. We present the case of a patient with three aspirated teeth following a motor vehicle accident.

Isolated second trimester uterine rupture following a motor vehicle accident

2021 ◽  
Vol 258 ◽  
pp. 461-463
Author(s):  
Tal Margaliot Kalifa ◽  
Misgav Rottenstreich ◽  
Eyal Mazaki ◽  
Hen Y. Sela ◽  
Schwartz Alon ◽  
...  
Keyword(s):  
Uterine Rupture ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Second Trimester ◽  
Vehicle Accident

scholarly journals Obstructive Sleep Apnea and Risk of Motor Vehicle Accident

2021 ◽  
Author(s):  
Gaia S. Pocobelli ◽  
Mary A. Akosile ◽  
Ryan N. Hansen ◽  
Joanna Eavey ◽  
Robert D. Wellman ◽  
...  
Keyword(s):  
Obstructive Sleep Apnea ◽  
Sleep Apnea ◽  
Motor Vehicle ◽  
Motor Vehicle Accident ◽  
Vehicle Accident ◽  
Obstructive Sleep
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