Document Detail


Air medical response to traumatic brain injury: a computer learning algorithm analysis.
MedLine Citation:
PMID:  18404053     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
BACKGROUND: The role of air medicine in traumatic brain injury (TBI) has been studied extensively using trauma registries but remains unclear. Learning algorithms, such as artificial neural networks (ANN), support vector machines (SVM), and decision trees, can identify relationships between data set variables but are not empirically useful for hypothesis testing. OBJECTIVE: To use ANN, SVM, and decision trees to explore the role of air medicine in TBI. METHODS: Patients with Head Abbreviated Injury Score 3+ were identified from our county trauma registry. Predictive models were generated using ANN, SVM, and decision trees. The three best-performing ANN models were used to calculate differential survival values (actual and predicted outcome) for each patient. In addition, predicted survival values with transport mode artificially input as "air" or "ground" were calculated for each patient to identify those who benefit from air transport. For SVM analysis, chi was used to compare the ratio of unexpected survivors to unexpected deaths for air- and ground-transported patients. Finally, decision tree analysis was used to explore the indications for various transport modes in optimized survival algorithms. RESULTS: A total of 11,961 patients were included. All three learning algorithms predicted a survival benefit with air transport across all patients, especially those with higher Head Abbreviated Injury Score or Injury Severity Score values, lower Glasgow Coma Scale scores, or hypotension. CONCLUSION: Air medical response in TBI seems to confer a survival advantage, especially in more critically injured patients.
Authors:
Daniel P Davis; Jeremy Peay; Benjamin Good; Michael J Sise; Frank Kennedy; A Brent Eastman; Thomas Velky; David B Hoyt
Related Documents :
16621473 - An accurate quantitative analysis of polymorphs based on artificial neural networks.
11496683 - Development of a real-time control strategy with artificial neural network for automati...
11205583 - A multiple focused probe approach for high intensity focused ultrasound based surgery.
Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  The Journal of trauma     Volume:  64     ISSN:  1529-8809     ISO Abbreviation:  J Trauma     Publication Date:  2008 Apr 
Date Detail:
Created Date:  2008-04-11     Completed Date:  2008-04-30     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0376373     Medline TA:  J Trauma     Country:  United States    
Other Details:
Languages:  eng     Pagination:  889-97     Citation Subset:  AIM; IM    
Affiliation:
Divisions of Trauma, University of California, San Diego, California, USA. davismd@cox.net
Export Citation:
APA/MLA Format     Download EndNote     Download BibTex
MeSH Terms
Descriptor/Qualifier:
Adolescent
Adult
Air Ambulances / utilization*
Algorithms*
Brain Injuries / diagnosis,  mortality,  therapy*
California
Cause of Death
Computer Simulation
Decision Trees*
Early Diagnosis
Emergency Medical Services / standards*,  trends
Female
Glasgow Coma Scale
Humans
Injury Severity Score
Male
Middle Aged
Registries
Risk Factors
Survival Analysis
Time Factors
Transportation of Patients

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


Previous Document:  Factors predictive of outcome in posttraumatic seizures.
Next Document:  Computed tomography alone for cervical spine clearance in the unreliable patient--are we there yet?