Acute Hypertensive Episodes Prediction

Nevo Itzhak, Aditya Nagori, Edo Lior, Maya Schvetz, Rakesh Lodha, Tavpritesh Sethi, Robert Moskovitch

Abstract: Predicting outbursts of hazardous medical conditions and its importance has arisen significantly in recent years, particularly in patients hospitalized in the Intensive Care Unit (ICU). In hospitals worldwide, patients are developing life-threatening complications, which might lead to organ dysfunctions and, if not treated properly, to death. In this study, we use patients’ longitudinal vital signs data from the ICUs, focusing on predicting Acute Hypertensive Episodes (AHE). In this study, two approaches were used for prediction: predicting continuously whether a patient will experience an AHE in a pre-defined time period ahead using an observation sliding window, or predicting whether it will generally occur during the ICU admission, given a fixed time period from the admission. Temporal abstraction was employed to transform the heterogeneous multivariate temporal data into a uniform representation of symbolic time intervals, and frequent Time Intervals Related Patterns (TIRPs), which are used as features for classification. For comparison, Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN) are used. Our results show that using frequent temporal patterns leads to a better AHE prediction.