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출혈성 쇼크를 일으킨 흰쥐에서 인공 신경망을 이용한 생존율 예측

Other Titles
 A Survival Prediction Model for Rats with Hemorrhagic Shock Using an Artificial Neural Network 
 이주형  ;  최재림  ;  정상원  ;  김덕원 
 Journal of the Korean Society of Emergency Medicine (대한응급의학회지), Vol.21(3) : 321-327, 2010 
Journal Title
 Journal of the Korean Society of Emergency Medicine (대한응급의학회지) 
Issue Date
Hemorrhagic shock ; Neural networks (computer) ; Survival rate ; Rats
Purpose: To achieve early diagnosis of hemorrhagic shock using a survival prediction model in rats. Methods: We measured heart rate, mean arterial pressure, respiration rate and temperature in 45 Sprague-Dawley rats, and obtained an artificial neural network model for predicting survival rates. Results: Area under the receiver operating characteristic (ROC) curves was 0.992. Applying the determined optimal boundary value of 0.47, the sensitivity and specificity of survival prediction were 98.4 and 96.6%, respectively. Conclusion: Because this artificial neural network predicts quite accurate survival rates for rats subjected to fixed-volume hemorrhagic shock, and does so with simple measurements of systolic blood pressure (SBP), mean arterial pressure (MAP), heart rate (HR), respiration rate (RR), and temperature (TEMP), it could provide early diagnosis and effective treatment for hemorrhagic shock if this artificial neural network is applicable to humans.
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1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers
Yonsei Authors
Kim, Deok Won(김덕원)
Lee, Ju Hyung(이주형)
Choi, Jae Lim(최재림)
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