Linked Life Data

12463843

Source:http://linkedlifedata.com/resource/pubmed/id/12463843

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2002-12-4
pubmed:abstractText
In intensive care physiological variables of the critically ill are measured and recorded in short time intervals. The proper extraction and interpretation of the information contained in this flood of information can hardly be done by experience alone. Intelligent alarm systems are needed to provide suitable bedside decision support. So far there is no commonly accepted standard for detecting the actual clinical state from the patient record. We use the statistical methodology of graphical models based on partial correlations for detecting time-varying relationships between physiological variables. Graphical models provide information on the relationships among physiological variables that is helpful e.g. for variable selection. Separate analyses for different pathophysiological states show that distinct clinical states are characterized by distinct partial correlation structures. Hence, this technique can provide new insights into physiological mechanisms.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1531-605X
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
340-4
pubmed:dateRevised
2009-11-18
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Detecting relationships between physiological variables using graphical models.
pubmed:affiliation
Surgical Department, Community Hospital Dortmund, Dortmund, Germany.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't