How Information Technologies Support the Intensive Care Systems: An Application of Mortality Prediction Model with Support Vector Machine

Background and Objective: Intensive care is very important in modern health care. Mortality prediction models are good outcome predictors for intensive care and resources allocation. Many research used the information technologies to construct new mortality prediction models. This study used the Support Vector Machine (SVM) to construct a better mortality prediction model. Methods: This study collected 695 patients (230 women and 465 men) who were admitted to the surgical intensive care unit in a 600-bed hospital as training data from January 1, 2005 to December31, 2006. Among the 695 patients, 538 (77.41%) patients were alive and 157 were dead (22.59%). This study selected the Gaussian RBF kernel to build a mortality prediction model with empirical data. All variables were included in this model. Results: The precision rate, recall rate and F-Measure of the SVM model were 0.899, 0.902 and 0.899, respectively. The area under ROC curve (AUR) of models was calculated. The SVM model (AUR=0.932) is better than SAPS II (AUR=0.883) and APACHE II (AUR=0.885) (p<0.01). Conclusion: The SVM can manage the twin peaks phenomenon which is one of the characteristics of health or medical data.