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rdf:type |
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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
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|
pubmed:issue |
10
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|
pubmed:dateCreated |
2005-9-27
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|
pubmed:abstractText |
Clustering by unsupervised learning with machine learning classifiers was shown to segment clusters of patterns in standard automated perimetry (SAP) for glaucoma in previous publications. In this study, unsupervised learning by independent component analysis decomposed SAP field patterns into axes, and the information represented by these axes was evaluated.
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pubmed:grant |
|
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pubmed:commentsCorrections |
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pubmed:language |
eng
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|
pubmed:journal |
|
|
pubmed:citationSubset |
IM
|
|
pubmed:status |
MEDLINE
|
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pubmed:month |
Oct
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pubmed:issn |
0146-0404
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pubmed:author |
pubmed-author:BodenCatherineC,
pubmed-author:BourneRupertR,
pubmed-author:BowdChristopherC,
pubmed-author:ChanKwokleungK,
pubmed-author:GoldbaumMichael HMH,
pubmed-author:HaoJiucangJ,
pubmed-author:LeeTe-WonTW,
pubmed-author:SamplePamela APA,
pubmed-author:SejnowskiTerrenceT,
pubmed-author:SpinakDavidD,
pubmed-author:WeinrebRobert NRN,
pubmed-author:ZangwillLindaL,
pubmed-author:ZhangZuohuaZ
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pubmed:issnType |
Print
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pubmed:volume |
46
|
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pubmed:owner |
NLM
|
|
pubmed:authorsComplete |
Y
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pubmed:pagination |
3676-83
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pubmed:dateRevised |
2010-12-17
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pubmed:meshHeading |
pubmed-meshheading:16186349-Artificial Intelligence,
pubmed-meshheading:16186349-Diagnostic Techniques, Ophthalmological,
pubmed-meshheading:16186349-Glaucoma, Open-Angle,
pubmed-meshheading:16186349-Humans,
pubmed-meshheading:16186349-Intraocular Pressure,
pubmed-meshheading:16186349-Middle Aged,
pubmed-meshheading:16186349-Optic Nerve Diseases,
pubmed-meshheading:16186349-Vision Disorders,
pubmed-meshheading:16186349-Visual Fields
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pubmed:year |
2005
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pubmed:articleTitle |
Using unsupervised learning with independent component analysis to identify patterns of glaucomatous visual field defects.
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pubmed:affiliation |
Ophthalmic Informatics Laboratory and Hamilton Glaucoma Center, Department of Ophthalmology, University of California at San Diego, La Jolla, 92093, USA. mgoldbaum@ucsd.edu
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pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, P.H.S.,
Research Support, Non-U.S. Gov't,
Research Support, N.I.H., Extramural
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