Predicting the occurrence of acute hypotensive episodes: The PhysioNet Challenge

The PhysioNet Challenge 2009 addresses the prediction of acute hypotensive episodes (AHEs), which are serious clinical events since they could result in multiple organ failure and eventually in death. This objective is pursued with two different events: (a) event 1: the separation of records with critical AHE (subgroup H1) in the forecast window (FW), the one-hour period immediately following a specified time T 0 , from records from patients with no documented AHEs at any time during their hospital stay (subgroup C1) and (b) event 2: the separation of records with an AHE in the FW (group H) from records without any AHE in the FW (group C). Both events have been approached, using a subset of information common to the whole dataset, through the extraction of significant features from the last hours before T 0 of the ABP and HR time series, linearly interpolated in the empty intervals and processed with a median filter for suppressing most artifacts. Decision tree classifiers based on these features have been designed for event 1 and 2, having better performances than classifiers based on support vector machine. The H1/C1 classifier (event 1) correctly classified all cases of the learning set (15 H1, 15 C1) producing also a perfect score on test set A. The H/C classifier (event 2) correctly classified 91.67% of the cases in the training set (30 H, 30 C) and obtained a score of 75% on test set B.