Machine learning classification of false-positive human immunodeficiency virus screening results

Machine learning classification of false-positive human immunodeficiency virus screening results

Background: Human immunodeficiency virus (HIV) screening has improved significantly in the past decade as we have implemented tests that include antigen detection of p24. Incorporation of p24 detection narrows the window from 4 to 2 weeks between infection acquisition and ability to detect infection...

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Bibliographic Details
Main Authors: Mahmoud Elkhadrawi, Bryan A Stevens, Bradley J Wheeler, Murat Akcakaya, Sarah Wheeler
Format: Article
Language:English
Published: Elsevier 2021-01-01
Series:Journal of Pathology Informatics
Subjects:
fifth-generation human immunodeficiency virus testing
human immunodeficiency virus
principal components analysis
serology
support vector machine
Online Access:http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2021;volume=12;issue=1;spage=46;epage=46;aulast=Elkhadrawi

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http://www.jpathinformatics.org/article.asp?issn=2153-3539;year=2021;volume=12;issue=1;spage=46;epage=46;aulast=Elkhadrawi

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