Decision tool for the early diagnosis of trauma patient hypovolemia.
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.
Chen
Liangyou
L
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. lchen@bioanalysis.org
McKenna
Thomas M
TM
Reisner
Andrew T
AT
Gribok
Andrei
A
Reifman
Jaques
J
D001185:Artificial Intelligence; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D004632:Emergency Medical Services / Q000379:methods*; D006801:Humans; D020896:Hypovolemia / Q000150:complications / Q000175:diagnosis*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014947:Wounds and Injuries / Q000150:complications / Q000175:diagnosis*