Trauma Data Collection and Mining to Enhance Combat Triage Decisions

Award Information
Agency:
Department of Defense
Branch
Army
Amount:
$69,995.00
Award Year:
2005
Program:
SBIR
Phase:
Phase I
Contract:
W81XWH-06-C-0037
Agency Tracking Number:
A052-180-1997
Solicitation Year:
2005
Solicitation Topic Code:
A05-180
Solicitation Number:
2005.2
Small Business Information
BARRON ASSOC., INC.
1410 Sachem Place, Suite 202, Charlottesville, VA, 22901
Hubzone Owned:
N
Socially and Economically Disadvantaged:
N
Woman Owned:
N
Duns:
120839477
Principal Investigator:
Todd Summers
Research Scientist
(434) 973-1215
summers@bainet.com
Business Contact:
David Ward
President
(434) 973-1215
barron@bainet.com
Research Institution:
n/a
Abstract
The fundamental objective of the research effort proposed herein is to provide guidance to combat medics in making field treatment and triage decisions. Combat medics must prioritize patients according to the need for: (1) immediate medical intervention, and/or (2) immediate evacuation and surgical intervention. Many life-threatening conditions -- most prominently acute hemorrhagic shock, followed by circulatory collapse -- are difficult to diagnose in the field, particularly in a timely fashion. This effort seeks to find predictive markers to enhance combat medic triage decisions and entails fulfillment of several objectives: (1) collection of a high-quality database of continuous physiologic data on trauma patients; (2) extraction of predictive features of relevant clinical outcomes from these data; and (3) development of a predictive methodology to aid in trauma triage decision making. Herein, Barron Associates, Inc. (BAI) and the University of Virginia (UVA) propose to collect continuous data on trauma patients via the Pegasus aeromedical transport service and the UVA Level 1 Trauma Center. BAI will apply sophisticated signal analysis and nonlinear dynamic pre-processing and feature extraction techniques to increase the likelihood of identifying useful predictive markers as inputs to estimation and classification neural networks, the latter of which predict relevant clinical outcomes.

* information listed above is at the time of submission.

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