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- R Fried, U Gather, and M Imhoff.
- Department of Statistics, University of Dortmund, D-44225 Dortmund, Germany.
- Proc AMIA Symp. 2001 Jan 1:184-8.
AbstractIn intensive care physiological variables of the critical-ly ill are measured and recorded in short time intervals. The existing alarm systems based on fixed thresholds produce a large number of false alarms. Usually the change of a variable over time is more informative than one pathological value at a particular time point. Intelligent alarm systems which detect important changes within a physiological time series are needed for suitable bedside decision support. There are various approaches to modeling time-dependent data and also several methodologies for pattern detection in time series. We compare several methodologies de-signed for online detection of measurement artifacts, level changes, and trends for a proper classification of the patient s state by means of a comparative case-study.
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