Time series analysis and outlier detection in intensive care data

In intensive care units, clinical information systems record a huge number of variables in the purpose of using them in medical decision making. Those variables are controlled by alarm systems based on fixed thresholds. This kind of systems produce alerts each time that a sudden shift as outlier, level change, change point or trend occurs and exceeds the threshold In practice, we can see that a big number of alarms are false; this is due to the presence of non-symptomatic outliers. In this paper we aim to present some methods that can be helpful to detect this kind of outliers. And later, they can be used in the development of intelligent alarm systems.