Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
8
pubmed:dateCreated
2006-9-1
pubmed:abstractText
Currently, approximately 80% of melanoma patients undergoing sentinel node biopsy (SNB) have negative sentinel lymph nodes (SLNs), and no prediction system is reliable enough to be implemented in the clinical setting to reduce the number of SNB procedures. In this study, the predictive power of support vector machine (SVM)-based statistical analysis was tested.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1068-9265
pubmed:author
pubmed:issnType
Print
pubmed:volume
13
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1113-22
pubmed:dateRevised
2007-7-18
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Support vector machine learning model for the prediction of sentinel node status in patients with cutaneous melanoma.
pubmed:affiliation
Clinica Chirurgica 2, Dipartimento di Scienze Oncologiche e Chirurgiche, Università di Padova, Via Giustiniani, 2, 35128 Padova, Italy. mocellins@hotmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't