title: Techniques to identify clinical contexts during automated data analysis.
abstract: The interpretation of automatically collected data to produce intelligent alarms and identify particular conditions is nearly impossible without identifying the specific context in which the data are obtained. Shifts in clinical context occur because of changes in the patient's physiologic state, or due to the passage of time, or due to changes imposed by therapeutic intervention such as surgery. Techniques to identify such changes in clinical context are discussed with particular attention to the application of cluster analysis, discriminant analysis, and statistical predictors. An example of these analyses applied to EEG data is presented, showing an unexpected hysteresis of EEG behavior in response to an hypoxic challenge.
authors.affiliation: Department of Anesthesiology/CCM, University of Pittsburgh School of Medicine, PA 15261.
authors.forename: M J
authors.initials: MJ
authors.lastname: Bloom
mesh_terms: D016000:Cluster Analysis; D003625:Data Collection; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D004569:Electroencephalography; D001330:Electronic Data Processing; D005583:Fourier Analysis; D006801:Humans; D008991:Monitoring, Physiologic