Predicting severe injury using vehicle telemetry data.
Identifieur interne : 000407 ( Main/Curation ); précédent : 000406; suivant : 000408Predicting severe injury using vehicle telemetry data.
Auteurs : Patricia Ayoung-Chee [États-Unis] ; Christopher D. Mack ; Robert Kaufman ; Eileen BulgerSource :
- The journal of trauma and acute care surgery [ 2163-0763 ] ; 2013.
Descripteurs français
- KwdFr :
- MESH :
English descriptors
- KwdEn :
- MESH :
- instrumentation : Telemetry.
- statistics & numerical data : Accidents, Traffic.
- Automobiles, Biomechanical Phenomena, Humans, Injury Severity Score, Models, Statistical, Sensitivity and Specificity.
Abstract
In 2010, the National Highway Traffic Safety Administration standardized collision data collected by event data recorders, which may help determine appropriate emergency medical service (EMS) response. Previous models (e.g., General Motors ) predict severe injury (Injury Severity Score [ISS] > 15) using occupant demographics and collision data. Occupant information is not automatically available, and 12% of calls from advanced automatic collision notification providers are unanswered. To better inform EMS triage, our goal was to create a predictive model only using vehicle collision data.
DOI: 10.1097/TA.0b013e31827a0bb6
PubMed: 23271095
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pubmed:23271095Le document en format XML
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Mack</name></author><author><name sortKey="Kaufman, Robert" sort="Kaufman, Robert" uniqKey="Kaufman R" first="Robert" last="Kaufman">Robert Kaufman</name></author><author><name sortKey="Bulger, Eileen" sort="Bulger, Eileen" uniqKey="Bulger E" first="Eileen" last="Bulger">Eileen Bulger</name></author></analytic><series><title level="j">The journal of trauma and acute care surgery</title><idno type="eISSN">2163-0763</idno><imprint><date when="2013" type="published">2013</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Accidents, Traffic (statistics & numerical data)</term><term>Automobiles</term><term>Biomechanical Phenomena</term><term>Humans</term><term>Injury Severity Score</term><term>Models, Statistical</term><term>Sensitivity and Specificity</term><term>Telemetry (instrumentation)</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Accidents de la circulation ()</term><term>Automobiles</term><term>Humains</term><term>Modèles statistiques</term><term>Phénomènes biomécaniques</term><term>Score de gravité des lésions traumatiques</term><term>Sensibilité et spécificité</term><term>Télémesure (instrumentation)</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="en"><term>Telemetry</term></keywords><keywords scheme="MESH" qualifier="statistics & numerical data" xml:lang="en"><term>Accidents, Traffic</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Automobiles</term><term>Biomechanical Phenomena</term><term>Humans</term><term>Injury Severity Score</term><term>Models, Statistical</term><term>Sensitivity and Specificity</term></keywords><keywords scheme="MESH" qualifier="instrumentation" xml:lang="fr"><term>Accidents de la circulation</term><term>Automobiles</term><term>Humains</term><term>Modèles statistiques</term><term>Phénomènes biomécaniques</term><term>Score de gravité des lésions traumatiques</term><term>Sensibilité et spécificité</term><term>Télémesure</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">In 2010, the National Highway Traffic Safety Administration standardized collision data collected by event data recorders, which may help determine appropriate emergency medical service (EMS) response. Previous models (e.g., General Motors ) predict severe injury (Injury Severity Score [ISS] > 15) using occupant demographics and collision data. Occupant information is not automatically available, and 12% of calls from advanced automatic collision notification providers are unanswered. To better inform EMS triage, our goal was to create a predictive model only using vehicle collision data.</div></front></TEI></record>Pour manipuler ce document sous Unix (Dilib)
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