Decision tool for the early diagnosis of trauma patient hypovolemia

J Biomed Inform. 2008 Jun;41(3):469-78. doi: 10.1016/j.jbi.2007.12.002. Epub 2008 Jan 18.


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<0.05). The simple computational requirements of ensemble classifiers will permit them to function in small fieldable devices for continuous monitoring of trauma patients.

Publication types

  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Artificial Intelligence*
  • Decision Support Systems, Clinical*
  • Diagnosis, Computer-Assisted / methods*
  • Emergency Medical Services / methods*
  • Humans
  • Hypovolemia / complications
  • Hypovolemia / diagnosis*
  • Reproducibility of Results
  • Sensitivity and Specificity
  • Wounds and Injuries / complications
  • Wounds and Injuries / diagnosis*