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rdf:type |
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lifeskim:mentions |
umls-concept:C0006142,
umls-concept:C0033325,
umls-concept:C0035647,
umls-concept:C0205225,
umls-concept:C0279781,
umls-concept:C0598941,
umls-concept:C0868995,
umls-concept:C1516048,
umls-concept:C1521761,
umls-concept:C1979963,
umls-concept:C2003903
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pubmed:issue |
10
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pubmed:dateCreated |
2003-9-23
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pubmed:abstractText |
The present study investigated complex time-dependent effects of routinely assessed factors on the risk of breast cancer recurrence over follow-up time, with a partial logistic artificial neural network (PLANN) model.
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pubmed:language |
eng
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pubmed:journal |
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pubmed:citationSubset |
IM
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pubmed:chemical |
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pubmed:status |
MEDLINE
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pubmed:month |
Oct
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pubmed:issn |
0923-7534
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pubmed:author |
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pubmed:issnType |
Print
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pubmed:volume |
14
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pubmed:owner |
NLM
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pubmed:authorsComplete |
Y
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pubmed:pagination |
1484-93
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pubmed:dateRevised |
2006-11-15
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pubmed:meshHeading |
pubmed-meshheading:14504047-Breast Neoplasms,
pubmed-meshheading:14504047-Female,
pubmed-meshheading:14504047-Humans,
pubmed-meshheading:14504047-Logistic Models,
pubmed-meshheading:14504047-Lymphatic Metastasis,
pubmed-meshheading:14504047-Middle Aged,
pubmed-meshheading:14504047-Neoplasm Recurrence, Local,
pubmed-meshheading:14504047-Neoplasm Staging,
pubmed-meshheading:14504047-Neural Networks (Computer),
pubmed-meshheading:14504047-Prognosis,
pubmed-meshheading:14504047-Receptors, Estrogen,
pubmed-meshheading:14504047-Receptors, Progesterone,
pubmed-meshheading:14504047-Retrospective Studies,
pubmed-meshheading:14504047-Risk Factors
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pubmed:year |
2003
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pubmed:articleTitle |
Prognosis in node-negative primary breast cancer: a neural network analysis of risk profiles using routinely assessed factors.
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pubmed:affiliation |
Unità di Statistica Medica e Biometria, Istituto Nazionale per lo Studio e la Cura dei Tumori, Milan, Italy. elia.biganzoli@istitutotumori.mi.it
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pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
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