A Survival Prediction Model for Rats with Hemorrhagic Shock Using an Artificial Neural Network.
- Author:
Ju Hyung LEE
1
;
Jae Lim CHOI
;
Sang Won CHUNG
;
Deok Won KIM
Author Information
1. The Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea. kdw@yuhs.ac
- Publication Type:Original Article
- Keywords:
Hemorrhagic shock;
Neural networks (computer);
Survival rate;
Rats
- MeSH:
Animals;
Arterial Pressure;
Blood Pressure;
Early Diagnosis;
Heart Rate;
Humans;
Neural Networks (Computer);
Rats;
Rats, Sprague-Dawley;
Respiratory Rate;
ROC Curve;
Sensitivity and Specificity;
Shock, Hemorrhagic;
Survival Rate
- From:Journal of the Korean Society of Emergency Medicine
2010;21(3):321-327
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
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.
