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18533119
Source:
http://linkedlifedata.com/resource/pubmed/id/18533119
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rdf:type
pubmed:Citation
lifeskim:mentions
umls-concept:C0598941
,
umls-concept:C0681842
,
umls-concept:C0877015
,
umls-concept:C2004457
pubmed:issue
2
pubmed:dateCreated
2008-8-4
pubmed:abstractText
The objective of this investigation was to test the ability of a feedforward artificial neural network (ANN) to differentiate patients who have pelvic organ prolapse (POP) from those who retain good pelvic organ support.
pubmed:grant
http://linkedlifedata.com/resource/pubmed/grant/5 T15 LM007438-04
pubmed:language
eng
pubmed:journal
http://linkedlifedata.com/resource/pubmed/journal/0370476
pubmed:citationSubset
AIM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1097-6868
pubmed:author
pubmed-author:AlmeidaJonas SJS
,
pubmed-author:JohnsonDonna DDD
,
pubmed-author:RobinsonChristopher JCJ
,
pubmed-author:SwiftStevenS
pubmed:issnType
Electronic
pubmed:volume
199
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
193.e1-6
pubmed:meshHeading
pubmed-meshheading:18533119-Aged
,
pubmed-meshheading:18533119-Algorithms
,
pubmed-meshheading:18533119-Case-Control Studies
,
pubmed-meshheading:18533119-Female
,
pubmed-meshheading:18533119-Humans
,
pubmed-meshheading:18533119-Middle Aged
,
pubmed-meshheading:18533119-Neural Networks (Computer)
,
pubmed-meshheading:18533119-ROC Curve
,
pubmed-meshheading:18533119-Sensitivity and Specificity
,
pubmed-meshheading:18533119-Uterine Prolapse
,
pubmed-meshheading:18533119-Valsalva Maneuver
pubmed:year
2008
pubmed:articleTitle
Prediction of pelvic organ prolapse using an artificial neural network.
pubmed:affiliation
Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Medical University of South Carolina, Charleston, SC, USA.
pubmed:publicationType
Journal Article
,
Research Support, N.I.H., Extramural