rdf:type |
|
lifeskim:mentions |
umls-concept:C0023185,
umls-concept:C0036454,
umls-concept:C0085862,
umls-concept:C0449432,
umls-concept:C0449774,
umls-concept:C0936012,
umls-concept:C1179435,
umls-concept:C1299583,
umls-concept:C1524073,
umls-concept:C1548799,
umls-concept:C1549571,
umls-concept:C1608386,
umls-concept:C1705248
|
pubmed:dateCreated |
2006-10-23
|
pubmed:abstractText |
We previously reported the use of clustering by unsupervised learning with machine learning classifiers to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma. In this study, the process of unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated.
|
pubmed:grant |
|
pubmed:commentsCorrections |
|
pubmed:language |
eng
|
pubmed:journal |
|
pubmed:citationSubset |
IM
|
pubmed:status |
MEDLINE
|
pubmed:issn |
1545-6110
|
pubmed:author |
|
pubmed:issnType |
Electronic
|
pubmed:volume |
103
|
pubmed:owner |
NLM
|
pubmed:authorsComplete |
Y
|
pubmed:pagination |
270-80
|
pubmed:dateRevised |
2010-11-18
|
pubmed:meshHeading |
pubmed-meshheading:17057807-Artificial Intelligence,
pubmed-meshheading:17057807-Bayes Theorem,
pubmed-meshheading:17057807-Diagnosis, Computer-Assisted,
pubmed-meshheading:17057807-Glaucoma,
pubmed-meshheading:17057807-Humans,
pubmed-meshheading:17057807-Learning,
pubmed-meshheading:17057807-Optic Nerve Diseases,
pubmed-meshheading:17057807-Severity of Illness Index,
pubmed-meshheading:17057807-Vision Disorders,
pubmed-meshheading:17057807-Visual Field Tests,
pubmed-meshheading:17057807-Visual Fields
|
pubmed:year |
2005
|
pubmed:articleTitle |
Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects.
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pubmed:affiliation |
Department of Ophthalmology, University of California, San Diego, La Jolla, California, USA.
|
pubmed:publicationType |
Journal Article,
Research Support, N.I.H., Extramural
|