대한의용생체공학회:의공학회지 (Journal of Biomedical Engineering Research)

  • 제33권1호
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  • Pages.1-7
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  • 2012
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  • 1229-0807(pISSN)
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  • 2288-9396(eISSN)
대한의용생체공학회 (The Korean Society of Medical and Biological Engineering)
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지원벡터기계를 이용한 출혈을 일으킨 흰쥐에서의 생존 예측

Survival Prediction of Rats with Hemorrhagic Shocks Using Support Vector Machine

  • 장경환 (연세대학교 생체공학협동과정) ;
  • 최재림 (연세대학교 생체공학협동과정) ;
  • 유태근 (연세대학교 의학전문 대학원) ;
  • 권민경 (연세대학교 의과대학 의학공학교실) ;
  • 김덕원 (연세대학교 생체공학협동과정)
  • Jang, K.H. (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Choi, J.L. (Graduate Program in Biomedical Engineering, Yonsei University) ;
  • Yoo, T.K. (Department of Medicine, Yonsei University College of Medicine) ;
  • Kwon, M.K. (Department of Medical Engineering, Yonsei University College of Medicine) ;
  • Kim, D.W. (Graduate Program in Biomedical Engineering, Yonsei University)
  • 투고 : 2011.10.18
  • 심사 : 2011.12.22
  • 발행 : 2012.03.30
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초록

Hemorrhagic shock is a common cause of death in emergency rooms. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. Therefore, the purpose of this study was to select an optimal survival prediction model using physiological parameters for the two analyzed periods: two and five minutes before and after the bleeding end. We obtained heart rates, mean arterial pressures, respiration rates and temperatures from 45 rats. These physiological parameters were used for the training and testing data sets of survival prediction models using an artificial neural network (ANN) and support vector machine (SVM). We applied a 5-fold cross validation method to avoid over-fitting and to select the optimal survival prediction model. In conclusion, SVM model showed slightly better accuracy than ANN model for survival prediction during the entire analysis period.

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