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pubmed-article:19277235pubmed:abstractTextTo test the following hypotheses: (1) eyes from individuals with human immunodeficiency virus (HIV) have electrophysiologic abnormalities that manifest as multifocal electroretinogram (mfERG) abnormalities; (2) the retinal effects of HIV in immune-competent HIV individuals differ from the effects in immune-incompetent HIV individuals; (3) strong machine learning classifiers (MLCs), like support vector machine (SVM), can learn to use mfERG abnormalities in the second-order kernel (SOK) to distinguish HIV from normal eyes; and (4) the mfERG abnormalities fall into patterns that can be discerned by MLCs. We applied a supervised MLC, SVM, to determine if mfERGs in eyes from patients with HIV differ from mfERGs in HIV-negative controls.lld:pubmed
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pubmed-article:19277235pubmed:dateRevised2009-11-18lld:pubmed
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pubmed-article:19277235pubmed:articleTitleAnalysis with support vector machine shows HIV-positive subjects without infectious retinitis have mfERG deficiencies compared to normal eyes.lld:pubmed
pubmed-article:19277235pubmed:affiliationOphthalmic Informatics Laboratory, University of California at San Diego, USA.lld:pubmed
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pubmed-article:19277235pubmed:publicationTypeResearch Support, Non-U.S. Gov'tlld:pubmed
pubmed-article:19277235pubmed:publicationTypeResearch Support, N.I.H., Extramurallld:pubmed
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