Classifying Alarms in Intensive Care - Analogy to Hypothesis Testing

Abstract

Monitoring devices in intensive care units observe a patient’s health status and trigger an alarm in critical situations. The alarm rules in commercially available monitoring systems are usually based on simple thresholds set by the clinical staff. Though there are some more advanced alarm rules integrated in modern monitoring devices, even for those, the false alarm rate is very high. Decision trees have proven suitable for alarm classification and false alarm reduction. Random forests which are ensembles of trees can improve the accuracy compared to single trees in many situations. In intensive care, the probability of misclassifying a situation in which an alarm is needed has to be controlled. Subject to this constraint the probability of misclassifying a situation in which no alarm should be given has to be minimized - an analogy to a hypothesis test for testing “situation is alarm relevant” vs. “situation is non alarm relevant” based on an ensemble of trees. This yields a classification rule for any given significance level, which is the probability of misclassifying alarm relevant situations. We apply this procedure to annotated physiological data recorded at an intensive care unit and generate rules for false alarm reduction.