Title

A Machine Learning And Data Mining Framework To Enable Evolutionary Improvement In Trauma Triage

Keywords

classification; subgroup discovery; trauma triage

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. © 2011 Springer-Verlag.

Publication Date

9-7-2011

Publication Title

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volume

6871 LNAI

Number of Pages

348-361

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1007/978-3-642-23199-5_26

Socpus ID

80052327097 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/80052327097

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