ArticleFrontiers in reproductive health2022
The role of machine learning in HIV risk prediction.
Article in Frontiers in reproductive health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.
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Who cites it
16 citing papers in PubMed, 1 synthesis or guideline pooled it, 32 citations in OpenAlex.
- Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2025Pooled it
- Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.AIDS (London, England) · 2025Trial
- Harnessing machine learning models to repurpose drugs targeting HIV-1 integrase, protease, and reverse transcriptase.Scientific reports · 2026Article
- The Applications of Machine Learning to Prognostic Modeling for Human Immunodeficiency Virus: A Protocol for a Scoping Review.Health science reports · 2026Article
- Machine Learning Application in Enhancing HIV Management and Treatment Outcomes: Revolutionizing HIV Infection.Current HIV research · 2026Review
- Human immunodeficiency virus recency testing coverage and partner-notification-services among people-living with human immunodeficiency virus in low- and middle-income countries.World journal of virology · 2025Article
- Proactive recognition and early detection in communities through targeted HIV screening.Scientific reports · 2025Article
- The Application of Machine Learning Algorithms to Predict HIV Testing Using Evidence from the 2002-2017 South African Adult Population-Based Surveys: An HIV Testing Predictive Model.Tropical medicine and infectious disease · 2025Article
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men.BMJ health & care informatics · 2025Article
- The status of machine learning in HIV testing in South Africa: a qualitative inquiry with stakeholders in Gauteng province.Frontiers in digital health · 2025Article
- Article
- A Human Immunodeficiency Virus Type 1 Risk Assessment Tool for Women Aged 15-49 Years in African Countries: A Pooled Analysis Across 15 Nationally Representative Surveys.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024Article
- Building gender-specific sexually transmitted infection risk prediction models using CatBoost algorithm and NHANES data.BMC medical informatics and decision making · 2024Article
- Predicting the individualized risk of human immunodeficiency virus infection among sexually active women in Ethiopia using a nomogram: prediction model development and validation.Frontiers in public health · 2024Article
- Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite advances in reducing HIV-related mortality, persistently high HIV incidence rates are undermining global efforts to end the epidemic by 2030. The UNAIDS Fast-track targets as well as other preventative strategies, such as pre-exposure prophylaxis, have been identified as priority areas to reduce the ongoing transmission threatening to undermine recent progress. Accurate and granular risk prediction is critical for these campaigns but is often lacking in regions where the burden is highest. Owing to their ability to capture complex interactions between data, machine learning and artificial intelligence algorithms have proven effective at predicting the risk of HIV infection in both high resource and low resource settings. However, interpretability of these algorithms presents a challenge to the understanding and adoption of these algorithms. In this perspectives article, we provide an introduction to machine learning and discuss some of the important considerations when choosing the variables used in model development and when evaluating the performance of different machine learning algorithms, as well as the role emerging tools such as Shapely Additive Explanations may play in helping understand and decompose these models in the context of HIV. Finally, we discuss some of the potential public health and clinical use cases for such decomposed risk assessment models in directing testing and preventative interventions including pre-exposure prophylaxis, as well as highlight the potential integration synergies with algorithms that predict the risk of sexually transmitted infections and tuberculosis.
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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.