ArticleClinical infectious diseases : an official publication of the Infectious Diseases Society of America2020
Machine Learning to Identify Persons at High-Risk of Human Immunodeficiency Virus Acquisition in Rural Kenya and Uganda.
Article in Clinical infectious diseases : an official publication of the Infectious Diseases Society of America, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01864603 (Sustainable East Africa Research in Community Health), which is not on this map. Cited by 45 papers, 4 of them syntheses 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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Sustainable East Africa Research in Community Health
Who cites it
45 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Risk prediction models for targeted testing of HIV, hepatitis B and hepatitis C: a systematic review and meta-analysis.BMC infectious diseases · 2025Pooled it
- 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
- Risk scores for predicting HIV incidence among adult heterosexual populations in sub-Saharan Africa: a systematic review and meta-analysis.Journal of the International AIDS Society · 2022Pooled it
- Acute and early HIV infection screening among men who have sex with men, a systematic review and meta-analysis.Journal of the International AIDS Society · 2020Pooled it
- Cost and cost-effectiveness of four different SARS-CoV-2 active surveillance strategies: evidence from a randomised control trial in Germany.The European journal of health economics : HEPAC : health economics in prevention and care · 2023Trial
- Medical Consultation and HIV Testing After AI-Based Symptom Check: Retrospective Cohort Study.Journal of medical Internet research · 2026Article
- An HIV-1 Risk Assessment Tool for Men Aged 15-59 years in 13 African Countries: A Pooled Analysis of Nationally Representative Surveys.Journal of acquired immune deficiency syndromes (1999) · 2026Article
- Artificial intelligence in HIV research: a structured review and task-oriented clinical framework.Frontiers in digital health · 2026Review
- External validation of a web- and artificial intelligence-based HIV/STI risk assessment tool: performance evaluation using data from Sydney sexual health centre.BMC infectious diseases · 2025Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- 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
- Role of Artificial Intelligence and Personalized Medicine in Enhancing HIV Management and Treatment Outcomes.Life (Basel, Switzerland) · 2025Review
- Machine learning to improve HIV screening using routine data in Kenya.Journal of the International AIDS Society · 2025Article
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States.PLOS global public health · 2025Article
- 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
- A Machine Learning Model for Identifying Sexual Health Influencers to Promote the Secondary Distribution of HIV Self-Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men in China: Quasi-Experimental Study.JMIR public health and surveillance · 2024Article
- Lightweight federated learning for STIs/HIV prediction.Scientific reports · 2024Article
- Addressing Unmet PrEP Needs in Women: Impact of a Laboratory-Driven Protocol at an Urban, Essential Hospital.Open forum infectious diseases · 2024Article
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Abstract
backgroundIn generalized epidemic settings, strategies are needed to prioritize individuals at higher risk of human immunodeficiency virus (HIV) acquisition for prevention services. We used population-level HIV testing data from rural Kenya and Uganda to construct HIV risk scores and assessed their ability to identify seroconversions.
methodsDuring 2013-2017, >75% of residents in 16 communities in the SEARCH study were tested annually for HIV. In this population, we evaluated 3 strategies for using demographic factors to predict the 1-year risk of HIV seroconversion: membership in ≥1 known "risk group" (eg, having a spouse living with HIV), a "model-based" risk score constructed with logistic regression, and a "machine learning" risk score constructed with the Super Learner algorithm. We hypothesized machine learning would identify high-risk individuals more efficiently (fewer persons targeted for a fixed sensitivity) and with higher sensitivity (for a fixed number targeted) than either other approach.
resultsA total of 75 558 persons contributed 166 723 person-years of follow-up; 519 seroconverted. Machine learning improved efficiency. To achieve a fixed sensitivity of 50%, the risk-group strategy targeted 42% of the population, the model-based strategy targeted 27%, and machine learning targeted 18%. Machine learning also improved sensitivity. With an upper limit of 45% targeted, the risk-group strategy correctly classified 58% of seroconversions, the model-based strategy 68%, and machine learning 78%.
conclusionsMachine learning improved classification of individuals at risk of HIV acquisition compared with a model-based approach or reliance on known risk groups and could inform targeting of prevention strategies in generalized epidemic settings. CLINICAL TRIALS REGISTRATION: NCT01864603.
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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.