Machine Learning Classification of False-Positive Human Immunodeficiency Virus Screening Results

Mahmoud Elkhadrawi; Bryan A Stevens; Bradley J Wheeler; Murat Akcakaya; Sarah Wheeler

doi:10.4103/jpi.jpi_7_21

Machine Learning Classification of False-Positive Human Immunodeficiency Virus Screening Results

J Pathol Inform. 2021 Nov 20:12:46. doi: 10.4103/jpi.jpi_7_21. eCollection 2021.

Authors

Mahmoud Elkhadrawi¹, Bryan A Stevens², Bradley J Wheeler³, Murat Akcakaya¹, Sarah Wheeler^{4

5}

Affiliations

¹ Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, PA, USA.
² Department of Pathology, Stanford University School of Medicine, Stanford, CA, USA.
³ Department of Pathology, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, USA.
⁴ Department of Pathology, School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA.
⁵ Department of Pathology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA.

Abstract

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, reducing unintentional spread of HIV. The fourth- and fifth-generation HIV (HIV5G) screening tests in low prevalence populations have high numbers of false-positive screens and it is unclear if orthogonal testing improves diagnostic and public health outcomes.

Methods: We used a cohort of 60,587 HIV5G screening tests with molecular and clinical correlates collected from 2016 to 2018 and applied machine learning to generate a classifier that could predict likely true and false positivity.

Results: The best classification was achieved by using support vector machines and transformation of results with principle component analysis. The final classifier had an accuracy of 94% for correct classification of false-positive screens and an accuracy of 92% for classification of true-positive screens.

Conclusions: Implementation of this classifier as a screening method for all HIV5G reactive screens allows for improved workflow with likely true positives reported immediately to reduce infection spread and initiate follow-up testing and treatment and likely false positives undergoing orthogonal testing utilizing the same specimen already drawn to reduce distress and follow-up visits. Application of machine learning to the clinical laboratory allows for workflow improvement and decision support to provide improved patient care and public health.

Keywords: Fifth-generation human immunodeficiency virus testing; human immunodeficiency virus; principal components analysis; serology; support vector machine.