Linked Life Data

1733603

Source:http://linkedlifedata.com/resource/pubmed/id/1733603

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
1992-2-28
pubmed:abstractText
Neural networks are a relatively new method of multivariate analysis. The purpose of this study was to investigate the ability of neural networks to differentiate benign from malignant breast conditions on the basis of the pattern of nine variables: patient age, total cholesterol, high-density lipoprotein cholesterol, triglycerides, apolipoprotein A-I, apolipoprotein B, albumin, the tumor marker CA15-3, and the Fossel index (measurement of methylene and methyl line-widths in proton NMR spectra). The laboratory analyses were made with blood plasma or serum specimens. The neural network was "trained" with 57 patients: 23 patients with breast malignancies and 34 patients with benign breast conditions. A neural network with nine input neurons, 15 hidden neurons, and two output neurons correctly classified all 57 patients. The ability of the network to predict the diagnoses of patients that it had no encountered in training was tested with a separate group (cross-validation group) of 20 patients. The network correctly predicted the diagnoses for 80% of these patients. For comparison we analyzed the same sets of 57 training patients and 20 cross-validation patients by quadratic discriminant function analysis. The quadratic discriminant function, calculated from the same 57 patients used to train the neural network, correctly classified 84% of the 57 patients, and correctly diagnosed 75% of the 20 cross-validation patients. The results suggest that neural networks are a potentially useful multivariate method for optimizing the diagnostic utility of laboratory data.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Jan
pubmed:issn
0009-9147
pubmed:author
pubmed:issnType
Print
pubmed:volume
38
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
34-8
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
1992
pubmed:articleTitle
Application of neural networks to the interpretation of laboratory data in cancer diagnosis.
pubmed:affiliation
Department of Pathology and Laboratory Medicine, Hospital of University of Pennsylvania, Philadelphia 19104-4283.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S.