지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
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.
목차
등록된 정보가 없습니다.
참고문헌 (24)
참고문헌 신청
A. Takasu / 2006 / Effect of induction rate for mild hypothermia on survival time during uncontrolled hemorrhagic shock in rats / J Trauma 61 (6) : 1330 ~ 1335
A.M. Prasad / 2006 / Newer classification and regression tree techniques: bagging and random forests for ecological prediction / Ecosystems 9 (2) : 181 ~ 199
C.D. Deakin / 1994 / AB or ABC: pre-hospital fluid management in major trauma / J Accid Emerg Med 11 (3) : 154 ~ 157
C.H. Hsieh / 2010 / Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks / Surgery
C.J. Murray / 1997 / Alternative projections of mortality and disability by cause 1990-2020: global burden of disease study / Lancet 349 (9064) : 1498 ~ 1504
A.M. Prasad / 2006 / Newer classification and regression tree techniques: bagging and random forests for ecological prediction / Ecosystems 9 (2) : 181 ~ 199
C.D. Deakin / 1994 / AB or ABC: pre-hospital fluid management in major trauma / J Accid Emerg Med 11 (3) : 154 ~ 157
C.H. Hsieh / 2010 / Novel solutions for an old disease: diagnosis of acute appendicitis with random forest, support vector machines, and artificial neural networks / Surgery
C.J. Murray / 1997 / Alternative projections of mortality and disability by cause 1990-2020: global burden of disease study / Lancet 349 (9064) : 1498 ~ 1504
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