Jang Kyung Hwan, Yoo Tae Keun, Choi Joon Yul, Nam Ki Chang, Choi Jae Lim, Kwon Min Kyung, Kim Deok Won
Graduate Program in Biomedical Engineering, Yonsei University, Seoul, Korea.
Annu Int Conf IEEE Eng Med Biol Soc. 2011;2011:91-4. doi: 10.1109/IEMBS.2011.6089904.
Hemorrhagic shock is the cause of one third of deaths resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physicians to treat patients successfully. The objective of this study was to select an optimal survival prediction model using physiological parameters from rats during our hemorrhagic experiment. These physiological parameters were used for the training and testing of survival prediction models using an artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the optimal survival prediction model according to performance measured by a 5-fold cross validation method. We selected an ANN with three hidden neurons and one hidden layer and an SVM with Gaussian kernel function as a trained survival prediction model. For the ANN model, the sensitivity, specificity, and accuracy of survival prediction were 97.8 ± 3.3 %, 96.3 ± 2.7 %, and 96.8 ± 1.7 %, respectively. For the SVM model, the sensitivity, specificity, and accuracy were 97.5 ± 2.9 %, 99.3 ± 1.1 %, and 98.5 ± 1.2 %, respectively. SVM was preferable to ANN for the survival prediction.
失血性休克是全球三分之一创伤死亡的原因。失血性休克的早期诊断使医生能够成功治疗患者。本研究的目的是在我们的出血实验中，使用大鼠的生理参数选择一个最佳的生存预测模型。这些生理参数用于使用人工神经网络（ANN）和支持向量机（SVM）的生存预测模型的训练和测试。为避免过拟合，我们根据通过5折交叉验证方法测量的性能选择最佳生存预测模型。我们选择了一个具有三个隐藏神经元和一个隐藏层的ANN以及一个具有高斯核函数的SVM作为训练后的生存预测模型。对于ANN模型，生存预测的敏感性、特异性和准确性分别为97.8±3.3%、96.3±2.7%和96.8±1.7%。对于SVM模型，敏感性、特异性和准确性分别为97.5±2.9%、99.3±1.1%和98.5±1.2%。在生存预测方面，SVM比ANN更优。
