18533119

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

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Subject	Predicate	Object	Context
pubmed-article:18533119	rdf:type	pubmed:Citation	lld:pubmed
pubmed-article:18533119	lifeskim:mentions	umls-concept:C0598941	lld:lifeskim
pubmed-article:18533119	lifeskim:mentions	umls-concept:C0877015	lld:lifeskim
pubmed-article:18533119	lifeskim:mentions	umls-concept:C0681842	lld:lifeskim
pubmed-article:18533119	lifeskim:mentions	umls-concept:C2004457	lld:lifeskim
pubmed-article:18533119	pubmed:issue	2	lld:pubmed
pubmed-article:18533119	pubmed:dateCreated	2008-8-4	lld:pubmed
pubmed-article:18533119	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.	lld:pubmed
pubmed-article:18533119	pubmed:grant	http://linkedlifedata.com/r...	lld:pubmed
pubmed-article:18533119	pubmed:language	eng	lld:pubmed
pubmed-article:18533119	pubmed:journal	http://linkedlifedata.com/r...	lld:pubmed
pubmed-article:18533119	pubmed:citationSubset	AIM	lld:pubmed
pubmed-article:18533119	pubmed:status	MEDLINE	lld:pubmed
pubmed-article:18533119	pubmed:month	Aug	lld:pubmed
pubmed-article:18533119	pubmed:issn	1097-6868	lld:pubmed
pubmed-article:18533119	pubmed:author	pubmed-author:SwiftStevenS	lld:pubmed
pubmed-article:18533119	pubmed:author	pubmed-author:AlmeidaJonas...	lld:pubmed
pubmed-article:18533119	pubmed:author	pubmed-author:RobinsonChris...	lld:pubmed
pubmed-article:18533119	pubmed:author	pubmed-author:JohnsonDonna...	lld:pubmed
pubmed-article:18533119	pubmed:issnType	Electronic	lld:pubmed
pubmed-article:18533119	pubmed:volume	199	lld:pubmed
pubmed-article:18533119	pubmed:owner	NLM	lld:pubmed
pubmed-article:18533119	pubmed:authorsComplete	Y	lld:pubmed
pubmed-article:18533119	pubmed:pagination	193.e1-6	lld:pubmed
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pubmed-article:18533119	pubmed:meshHeading	pubmed-meshheading:18533119...	lld:pubmed
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pubmed-article:18533119	pubmed:meshHeading	pubmed-meshheading:18533119...	lld:pubmed
pubmed-article:18533119	pubmed:meshHeading	pubmed-meshheading:18533119...	lld:pubmed
pubmed-article:18533119	pubmed:meshHeading	pubmed-meshheading:18533119...	lld:pubmed
pubmed-article:18533119	pubmed:meshHeading	pubmed-meshheading:18533119...	lld:pubmed
pubmed-article:18533119	pubmed:meshHeading	pubmed-meshheading:18533119...	lld:pubmed
pubmed-article:18533119	pubmed:year	2008	lld:pubmed
pubmed-article:18533119	pubmed:articleTitle	Prediction of pelvic organ prolapse using an artificial neural network.	lld:pubmed
pubmed-article:18533119	pubmed:affiliation	Department of Obstetrics and Gynecology, Division of Maternal-Fetal Medicine, Medical University of South Carolina, Charleston, SC, USA.	lld:pubmed
pubmed-article:18533119	pubmed:publicationType	Journal Article	lld:pubmed
pubmed-article:18533119	pubmed:publicationType	Research Support, N.I.H., Extramural	lld:pubmed
http://linkedlifedata.com/r...	pubmed:referesTo	pubmed-article:18533119	lld:pubmed