Complex, often high-dimensional time series are observed in medical applications such as intensive care. We review statistical tools for intelligent alarm systems, which are helpful for guiding medical decision-making in time-critical situations. The procedures described can also be applied for decision support or in closed-loop controllers. Robust time series filters allow one to extract a signal in the form of a time-varying trend with little or no delay. Additional rules—based, for instance, on suitably designed statistical tests—can be incorporated to preserve or detect interesting patterns such as level shifts or trend changes. Statistical pattern detection is a useful preprocessing step for decision-support systems. Dimension reduction techniques allow the compression of the often high-dimensional time series into a few variables containing most of the information inherent in the observed data. Combining such techniques with tools for analyzing the relationships among the variables in the form of large partial correlations or similar trend behavior improves the interpretability of the extracted variables and provides information that is thus meaningful to physicians.


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