Online pattern recognition in intensive care medicine.
In 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.
Fried
R
R
Department of Statistics, University of Dortmund, D-44225 Dortmund, Germany.
Gather
U
U
Imhoff
M
M
D000368:Aged; D003422:Critical Care / Q000379:methods*; D004868:Equipment Failure; D005260:Female; D006801:Humans; D007362:Intensive Care Units; D016014:Linear Models; D008297:Male; D008991:Monitoring, Physiologic / Q000295:instrumentation / Q000379:methods*; D010363:Pattern Recognition, Automated; D013995:Time