Decision tool for the early diagnosis of trauma patient hypovolemia

  • Authors:

  • Liangyou Chen;Thomas M. McKenna;Andrew T. Reisner;Andrei Gribok;Jaques Reifman

  • Affiliations:

  • Bioinformatics Cell, Telemedicine and Advanced Technology Research Center (TATRC), Building 363 Miller Drive, US Army Medical Research and Materiel Command (USAMRMC), Frederick, MD 21702-5012, USA;Bioinformatics Cell, Telemedicine and Advanced Technology Research Center (TATRC), Building 363 Miller Drive, US Army Medical Research and Materiel Command (USAMRMC), Frederick, MD 21702-5012, USA;Bioinformatics Cell, Telemedicine and Advanced Technology Research Center (TATRC), Building 363 Miller Drive, US Army Medical Research and Materiel Command (USAMRMC), Frederick, MD 21702-5012, USA ...;Bioinformatics Cell, Telemedicine and Advanced Technology Research Center (TATRC), Building 363 Miller Drive, US Army Medical Research and Materiel Command (USAMRMC), Frederick, MD 21702-5012, USA ...;Bioinformatics Cell, Telemedicine and Advanced Technology Research Center (TATRC), Building 363 Miller Drive, US Army Medical Research and Materiel Command (USAMRMC), Frederick, MD 21702-5012, USA

  • Venue:
  • Journal of Biomedical Informatics
  • Year:
  • 2008

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Abstract

We present a classifier for use as a decision assist tool to identify a hypovolemic state in trauma patients during helicopter transport to a hospital, when reliable acquisition of vital-sign data may be difficult. The decision tool uses basic vital-sign variables as input into linear classifiers, which are then combined into an ensemble classifier. The classifier identifies hypovolemic patients with an area under a receiver operating characteristic curve (AUC) of 0.76 (standard deviation 0.05, for 100 randomly-reselected patient subsets). The ensemble classifier is robust; classification performance degrades only slowly as variables are dropped, and the ensemble structure does not require identification of a set of variables for use as best-feature inputs into the classifier. The ensemble classifier consistently outperforms best-features-based linear classifiers (the classification AUC is greater, and the standard deviation is smaller, p