Probability distribution pattern analysis and its application in the Acute Hypotensive Episodes prediction

At present many hospitals have to deal with the patient's care and nursing for Acute Hypotensive Episodes (AHE) occurring in Intensive Care Units. AHE can cause fainting or shock suddenly, leading to irreversibility organ damage, and even death. Therefore, forecasting of occurrence of AHE is of practical value. However, the prediction of clinical AHE largely depends on the doctors' experience, which cannot guarantee the high rate of success. It is thus very meaningful for the clinical care to use appropriate methods to predict the AHE with an automatic and reliable method. In this study, a Probability Distribution Patterns Analysis (PDPA) method is presented to solve the time series prediction problem of AHE. In the first phase, the features are extracted from the PDPA in the global and integral time series, and the partial local time series in the fixed time window. In the second phase, the proposed algorithm combining Genetic Algorithm (GA) and Support Vector Machine (SVM), namely GA-SVM is adopted to select the vital features for the effective classification. In order to demonstrate the generality of our method, we also conduct experiments on a classical time series problem-Control Chart Patterns (CCPs) multi-class time series, which is a benchmark problem in the process control. For CCPs problem, the experimental results demonstrate that the proposed method outperforms several traditional methods. The obtained accuracy is 98.65%, which is superior to listed previous works using the same CCPs model. For AHE classification and forecasting, the methodology is applied in two data sets, a small data set (37 records) and a big one (2892 records). The test accuracy of 89.19%, sensitivity of 91.67%, specificity of 88% in the small data set, and a test accuracy of 80.76%, sensitivity of 78.19%, specificity of 81.51% in the big data set are achieved with the classification model.