Document Detail


Dimension reduction for highdimensional online-monitoring data in intensive care.
MedLine Citation:
PMID:  11187656     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
Nowadays high dimensional data in intensive care medicine can be captured, stored, and retrieved with the help of clinical information systems. Intelligent alarm systems are needed for an adequate bedside decision support, in the course of which the detection of qualitative patterns in physiologic monitoring data such as outliers, level changes, or trends aims at a proper classification of the patients state. Statistical time series techniques have already been applied successfully to the analysis of single physiological variables. The simultaneous online analysis of the multivariate patient curve yields further challenges. We describe methods for reducing the dimension and for keeping the computational efforts necessary for monitoring low. We present preliminary results of an ongoing study on monitoring critically ill patients.
Authors:
M Bauer; R Fried; U Gather; M Imhoff
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Publication Detail:
Type:  Journal Article    
Journal Detail:
Title:  Studies in health technology and informatics     Volume:  77     ISSN:  0926-9630     ISO Abbreviation:  Stud Health Technol Inform     Publication Date:  2000  
Date Detail:
Created Date:  2001-01-18     Completed Date:  2001-02-22     Revised Date:  2004-11-17    
Medline Journal Info:
Nlm Unique ID:  9214582     Medline TA:  Stud Health Technol Inform     Country:  Netherlands    
Other Details:
Languages:  eng     Pagination:  767-71     Citation Subset:  T    
Affiliation:
Department of Statistics, University of Dortmund, Germany.
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MeSH Terms
Descriptor/Qualifier:
Adult
Aged
Aged, 80 and over
Artificial Intelligence*
Data Collection
Decision Support Systems, Clinical
Equipment Failure
Female
Hospitals, Teaching
Humans
Intensive Care*
Male
Middle Aged
Monitoring, Physiologic*
Online Systems*

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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