| pubmed-article:20564067 | rdf:type | pubmed:Citation | lld:pubmed |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0006142 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0006751 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0242406 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0012632 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0596244 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0681916 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0750572 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C0680844 | lld:lifeskim |
| pubmed-article:20564067 | lifeskim:mentions | umls-concept:C2004457 | lld:lifeskim |
| pubmed-article:20564067 | pubmed:issue | 14 | lld:pubmed |
| pubmed-article:20564067 | pubmed:dateCreated | 2010-7-12 | lld:pubmed |
| pubmed-article:20564067 | pubmed:abstractText | Discriminating malignant breast lesions from benign ones and accurately predicting the risk of breast cancer for individual patients are crucial to successful clinical decisions. In the past, several artificial neural network (ANN) models have been developed for breast cancer-risk prediction. All studies have reported discrimination performance, but not one has assessed calibration, which is an equivalently important measure for accurate risk prediction. In this study, the authors have evaluated whether an artificial neural network (ANN) trained on a large prospectively collected dataset of consecutive mammography findings can discriminate between benign and malignant disease and accurately predict the probability of breast cancer for individual patients. | lld:pubmed |
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| pubmed-article:20564067 | pubmed:language | eng | lld:pubmed |
| pubmed-article:20564067 | pubmed:journal | http://linkedlifedata.com/r... | lld:pubmed |
| pubmed-article:20564067 | pubmed:citationSubset | AIM | lld:pubmed |
| pubmed-article:20564067 | pubmed:status | MEDLINE | lld:pubmed |
| pubmed-article:20564067 | pubmed:month | Jul | lld:pubmed |
| pubmed-article:20564067 | pubmed:issn | 0008-543X | lld:pubmed |
| pubmed-article:20564067 | pubmed:author | pubmed-author:BurnsideEliza... | lld:pubmed |
| pubmed-article:20564067 | pubmed:author | pubmed-author:AlagozOguzhan... | lld:pubmed |
| pubmed-article:20564067 | pubmed:author | pubmed-author:KahnCharles... | lld:pubmed |
| pubmed-article:20564067 | pubmed:author | pubmed-author:ShavlikJude... | lld:pubmed |
| pubmed-article:20564067 | pubmed:author | pubmed-author:ChhatwalJagpr... | lld:pubmed |
| pubmed-article:20564067 | pubmed:author | pubmed-author:AyerTurgayT | lld:pubmed |
| pubmed-article:20564067 | pubmed:copyrightInfo | Copyright (c) 2010 American Cancer Society. | lld:pubmed |
| pubmed-article:20564067 | pubmed:issnType | Print | lld:pubmed |
| pubmed-article:20564067 | pubmed:day | 15 | lld:pubmed |
| pubmed-article:20564067 | pubmed:volume | 116 | lld:pubmed |
| pubmed-article:20564067 | pubmed:owner | NLM | lld:pubmed |
| pubmed-article:20564067 | pubmed:authorsComplete | Y | lld:pubmed |
| pubmed-article:20564067 | pubmed:pagination | 3310-21 | lld:pubmed |
