| Neural networks prediction of preterm delivery with first trimester bleeding. | |
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MedLine Citation:
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PMID: 20449599 Owner: NLM Status: In-Data-Review |
Abstract/OtherAbstract:
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OBJECTIVE: This paper illustrates a retrospective study of the outcome of those pregnancies that continued from an initial episode of bleeding in first trimester. METHODS: Neural networks is used for the prediction of preterm delivery, using various inputs such as the age of women, gestational age when the bleeding occurred, duration of the bleeding days, amount of bleeding, number of episodes, presence or absence of hematoma and placentation position. RESULTS: The success rate of prediction obtained using the feed forward backpropogation network is 70%. Hence, this model can be used to identify women at the risk of premature delivery for planning antenatal care and clinical interventions in pregnancy. |
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Authors:
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U Elaveyini; S Prasanna Devi; K Suryaprakasa Rao |
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Publication Detail:
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Type: Journal Article Date: 2010-05-07 |
Journal Detail:
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Title: Archives of gynecology and obstetrics Volume: 283 ISSN: 1432-0711 ISO Abbreviation: Arch. Gynecol. Obstet. Publication Date: 2011 May |
Date Detail:
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Created Date: 2011-04-26 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8710213 Medline TA: Arch Gynecol Obstet Country: Germany |
Other Details:
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Languages: eng Pagination: 971-9 Citation Subset: IM |
Affiliation:
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Department of Obstetrics and Gynecology, CSI Rainy Multispecialty Hospital, Chennai, India. |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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