pubmed-article:11464033 | rdf:type | pubmed:Citation | lld:pubmed |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0376358 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0332307 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0005558 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0242406 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0032790 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0027646 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0681842 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C0220922 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C2004457 | lld:lifeskim |
pubmed-article:11464033 | lifeskim:mentions | umls-concept:C1527178 | lld:lifeskim |
pubmed-article:11464033 | pubmed:issue | 5 | lld:pubmed |
pubmed-article:11464033 | pubmed:dateCreated | 2001-7-20 | lld:pubmed |
pubmed-article:11464033 | pubmed:abstractText | The choice of therapy for prostatic cancer should depend on a rational preoperative estimate of tumor stage. Artificial neural networks were used to predict postoperative staging of prostatic cancer from sextant biopsies and routinely available preoperative data. | lld:pubmed |
pubmed-article:11464033 | pubmed:language | eng | lld:pubmed |
pubmed-article:11464033 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
pubmed-article:11464033 | pubmed:citationSubset | IM | lld:pubmed |
pubmed-article:11464033 | pubmed:status | MEDLINE | lld:pubmed |
pubmed-article:11464033 | pubmed:month | May | lld:pubmed |
pubmed-article:11464033 | pubmed:issn | 0302-2838 | lld:pubmed |
pubmed-article:11464033 | pubmed:author | pubmed-author:HautmannRR | lld:pubmed |
pubmed-article:11464033 | pubmed:author | pubmed-author:MattfeldtTT | lld:pubmed |
pubmed-article:11464033 | pubmed:author | pubmed-author:GottfriedH... | lld:pubmed |
pubmed-article:11464033 | pubmed:author | pubmed-author:KestlerH AHA | lld:pubmed |
pubmed-article:11464033 | pubmed:issnType | Print | lld:pubmed |
pubmed-article:11464033 | pubmed:volume | 39 | lld:pubmed |
pubmed-article:11464033 | pubmed:owner | NLM | lld:pubmed |
pubmed-article:11464033 | pubmed:authorsComplete | Y | lld:pubmed |
pubmed-article:11464033 | pubmed:pagination | 530-6; discussion 537 | lld:pubmed |
pubmed-article:11464033 | pubmed:dateRevised | 2004-11-17 | lld:pubmed |
pubmed-article:11464033 | pubmed:meshHeading | pubmed-meshheading:11464033... | lld:pubmed |
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pubmed-article:11464033 | pubmed:meshHeading | pubmed-meshheading:11464033... | lld:pubmed |
pubmed-article:11464033 | pubmed:year | 2001 | lld:pubmed |
pubmed-article:11464033 | pubmed:articleTitle | Prediction of postoperative prostatic cancer stage on the basis of systematic biopsies using two types of artificial neural networks. | lld:pubmed |
pubmed-article:11464033 | pubmed:affiliation | Department of Pathology, University of Ulm, Germany. torsten.mattfeldt@medizin.uni-ulm.de | lld:pubmed |
pubmed-article:11464033 | pubmed:publicationType | Journal Article | lld:pubmed |