ArticleiScience2024
Machine learning models based on fluid immunoproteins that predict non-AIDS adverse events in people with HIV.
Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Article
- Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6 months.AIDS (London, England) · 2025Article
- Plasma anti-CD4 IgG levels are associated with poor immune recovery in people with HIV initiating antiretroviral therapy.AIDS (London, England) · 2025Article
- Machine learning-based prediction of mortality risk in AIDS patients with comorbid common AIDS-related diseases or symptoms.Frontiers in public health · 2025Article
Corrections and comments
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Authors and funding
9 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.