Linked Life Data

12075672

Source:http://linkedlifedata.com/resource/pubmed/id/12075672

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2
pubmed:dateCreated
2002-6-21
pubmed:abstractText
In earlier work, the research group successfully used artificial neural networks (ANNs) to estimate ventilation duration for adult intensive care unit (ICU) patients. The ANNs performed well in terms of correct classification rate (CCR) and average squared error (ASE) classifying the outcome into two classes: whether patients were ventilated for less than/equal to or for more than 8 h (< or >). The objective of new work was to apply this adult model to the estimation of ventilation with neonatal ICU (NICU) patient records. The performance obtained with the neonatal patients was comparable to that previously found with the adult database, again as measured in terms of a maximum CCR and a minimum ASE. The effectiveness of using the weight-elimination technique in controlling overfitting was again validated for the neonatal patients as it had been for our adult patients. It was concluded that the approach developed for ICU adult patients was also successfully applied to a different medical environment: neonatal ICU patients.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Jun
pubmed:issn
1089-7771
pubmed:author
pubmed:issnType
Print
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
188-91
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Extending ventilation duration estimations approach from adult to neonatal intensive care patients using artificial neural networks.
pubmed:affiliation
School of Information Technology and Engineering, University of Ottawa, ON, Canada.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Validation Studies