Source:http://linkedlifedata.com/resource/pubmed/id/11760100
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
6
|
| pubmed:dateCreated |
2001-12-10
|
| pubmed:abstractText |
This study examined obstetricians' decisions to perform or not to perform cesarean sections. The aim was to determine whether an artificial neural network could be constructed to accurately and reliably predict the birthing mode decisions of expert clinicians and to elucidate which factors were most important in deciding the birth mode.
|
| pubmed:grant | |
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:issn |
0272-989X
|
| pubmed:author | |
| pubmed:issnType |
Print
|
| pubmed:volume |
21
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
433-43
|
| pubmed:dateRevised |
2007-11-14
|
| pubmed:meshHeading |
pubmed-meshheading:11760100-Decision Making,
pubmed-meshheading:11760100-Female,
pubmed-meshheading:11760100-Humans,
pubmed-meshheading:11760100-Neural Networks (Computer),
pubmed-meshheading:11760100-Obstetrics,
pubmed-meshheading:11760100-Pregnancy,
pubmed-meshheading:11760100-ROC Curve,
pubmed-meshheading:11760100-Sensitivity and Specificity
|
| pubmed:articleTitle |
Understanding birthing mode decision making using artificial neural networks.
|
| pubmed:affiliation |
Graduate Program in Health Services Administration, Xavier University, Cincinnati, Ohio, 45207-7331, USA. macdowel@xavier.xu.edu
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| pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, P.H.S.,
Research Support, Non-U.S. Gov't
|