Journal of Biomedical Engineering Research (대한의용생체공학회:의공학회지)

The Korean Society of Medical and Biological Engineering (대한의용생체공학회)
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A Survival Prediction Model of Rats in Uncontrolled Acute Hemorrhagic Shock Using the Random Forest Classifier

랜덤 포리스트를 이용한 비제어 급성 출혈성 쇼크의 흰쥐에서의 생존 예측

  • 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)
  • 최준열 (연세대학교 의과대학 의학공학교실) ;
  • 김성권 (연세대학교 의과대학 의학공학교실) ;
  • 구정모 (연세대학교 의과대학 의학공학교실) ;
  • 김덕원 (연세대학교 의과대학 의학공학교실)
  • Received : 2012.08.20
  • Accepted : 2012.09.10
  • Published : 2012.09.30
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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

References

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