18533119

Source:http://linkedlifedata.com/resource/pubmed/id/18533119

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Predicate	Object
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