Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV

AIDS Clinical Trials Group NWCS 411 study team

Research output: Contribution to journalArticlepeer-review

Abstract

Despite the success of antiretroviral therapy (ART), individuals with HIV remain at risk for experiencing non-AIDS adverse events (NAEs), including cardiovascular complications and malignancy. Several surrogate immune biomarkers in blood have shown predictive value in predicting NAEs; however, composite panels generated using machine learning may provide a more accurate advancement for monitoring and discriminating NAEs. In a nested case-control study, we aimed to develop machine learning models to discriminate cases (experienced an event) and matched controls using demographic and clinical characteristics alongside 49 plasma immunoproteins measured prior to and post-ART initiation. We generated support vector machine (SVM) classifier models for high-accuracy discrimination of individuals aged 30–50 years who experienced non-fatal NAEs at pre-ART and one-year post-ART. Extreme gradient boosting generated a high-accuracy model at pre-ART, while K-nearest neighbors performed poorly all around. SVM modeling may offer guidance to improve disease monitoring and elucidate potential therapeutic interventions.

Original languageEnglish (US)
Article number109945
Pages (from-to)109945
JournaliScience
Volume27
Issue number6
DOIs
StatePublished - Jun 21 2024
Externally publishedYes

Keywords

  • Health sciences
  • Immunology
  • Machine learning
  • Public health
  • Virology

ASJC Scopus subject areas

  • General

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