A machine learning and data mining framework to enable evolutionary improvement in trauma triage

Authors:
Douglas A. Talbert;Matt Honeycutt;Steve Talbert
Affiliations:
Tennessee Technological University, Cookeville TN;InRAD, LLC, Knoxville, TN;University of Central Florida, Orlando, FL
Venue:
MLDM'11 Proceedings of the 7th international conference on Machine learning and data mining in pattern recognition
Year:
2011

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Abstract

Trauma triage seeks to match injured patients with appropriate healthcare resources. Mistriage can be costly both in terms of money and lives. This paper proposes and evaluates a comprehensive model that uses both machine learning and data mining to support the process of trauma triage. The proposed model is more dynamic and adaptive than the typical guideline-based approach, and it incorporates a computer-assisted feedback loop to support clinician efforts to improve triage accuracy. This paper uses three years of retrospective data to compare multiple machine learning algorithms to the current standard triage decision guidelines. Then, the triage classifications from one of those experiments are used as input to demonstrate the potential of our data mining algorithm to provide a mapping between patient type and classifier performance.