Decision tool for the early diagnosis of trauma patient hypovolemia

We present a classifier for use as a decision assist tool to identify a hypovolernic 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 hypovolernic 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 (toes not require identification of a set of variables for use as best-feature inputs into the classifier. The ensemble classifier consistently outperforms bestfeatures-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. (c) 2007 Elsevier Inc. All rights reserved.