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http://dx.doi.org/10.9718/JBER.2012.33.3.148

A Survival Prediction Model of Rats in Uncontrolled Acute Hemorrhagic Shock Using the Random Forest Classifier  

Choi, J.Y. (Department of Medical Engineering, Yonsei University College of Medicine)
Kim, S.K. (Department of Medical Engineering, Yonsei University College of Medicine)
Koo, J.M. (Department of Medical Engineering, Yonsei University College of Medicine)
Kim, D.W. (Department of Medical Engineering, Yonsei University College of Medicine)
Publication Information
Journal of Biomedical Engineering Research / v.33, no.3, 2012 , pp. 148-154 More about this Journal
Abstract
Hemorrhagic shock is a primary cause of deaths resulting from injury in the world. Although many studies have tried to diagnose accurately hemorrhagic shock in the early stage, such attempts were not successful due to compensatory mechanisms of humans. The objective of this study was to construct a survival prediction model of rats in acute hemorrhagic shock using a random forest (RF) model. Heart rate (HR), mean arterial pressure (MAP), respiration rate (RR), lactate concentration (LC), and peripheral perfusion (PP) measured in rats were used as input variables for the RF model and its performance was compared with that of a logistic regression (LR) model. Before constructing the models, we performed 5-fold cross validation for RF variable selection, and forward stepwise variable selection for the LR model to examine which variables were important for the models. For the LR model, sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (ROC-AUC) were 0.83, 0.95, 0.88, and 0.96, respectively. For the RF models, sensitivity, specificity, accuracy, and AUC were 0.97, 0.95, 0.96, and 0.99, respectively. In conclusion, the RF model was superior to the LR model for survival prediction in the rat model.
Keywords
hemorrhagic shock; random forest; logistic regression; variable selection; survival prediction;
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