Patient Outcome Prediction with Heart Rate Variability and Vital Signs

The ability to predict patient outcomes is important for clinical triage, which is the process of assessing severity and assigning appropriate priority of treatment for large numbers of patients. In this study, we present an automatic prognosis system for patient outcome prediction with heart rate variability (HRV) and traditional vital signs. Support vector machine (SVM) and extreme learning machine (ELM) are employed as predictors, and SVM with linear kernel is reported to perform the best in general. In the experiments, the combination of HRV measures and vital signs is found to be more closely associated with patient outcome than either HRV or vital signs. Moreover, two new segment based methods are proposed to improve the predictive accuracy, where several sets of HRV measures are calculated from non-overlapped segments for each patient and final decision is made through the majority voting rule. The results reveal that the segment based methods are able to enhance the prediction performance significantly.