Linked Life Data

16324907

Source:http://linkedlifedata.com/resource/pubmed/id/16324907

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2005-12-5
pubmed:abstractText
Hypoplastic left heart syndrome (HLHS) affects infants and is uniformly fatal without surgical palliation. Post-surgery mortality rates are highly variable and dependent on postoperative management. A data acquisition system was developed for collection of 73 physiologic, laboratory, and nurse-assessed parameters. The acquisition system was designed for the collection on numerous patients. Data records were created at 30s intervals. An expert-validated wellness score was computed for each data record. To efficiently analyze the data, a new metric for assessment of data utility, the combined classification quality measure, was developed. This measure assesses the impact of a feature on classification accuracy without performing computationally expensive cross-validation. The proposed measure can be also used to derive new features that enhance classification accuracy. The knowledge discovery approach allows for instantaneous prediction of interventions for the patient in an intensive care unit. The discovered knowledge can improve care of complex to manage infants by the development of an intelligent bedside advisory system.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0010-4825
pubmed:author
pubmed:issnType
Print
pubmed:volume
36
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
21-40
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Hypoplastic left heart syndrome: knowledge discovery with a data mining approach.
pubmed:affiliation
Intelligent Systems Laboratory, MIE 3131, Seamans Center, The University of Iowa, Iowa City, Iowa 52242 - 1527, USA. andrew-kusiak@uiowa.edu
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't