ArticleScientific reports2020
Predictive Analytics for Retention in Care in an Urban HIV Clinic.
Article in Scientific reports, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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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.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Evaluating predictive performance, validity, and applicability of machine learning models for predicting HIV treatment interruption: a systematic review.BMC global and public health · 2025Pooled it
- Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.AIDS (London, England) · 2025Trial
- Visit-level prediction of missed HIV appointments using machine learning and transformer-based models in Uganda.BMC medical informatics and decision making · 2026Article
- Prediction of First and Multiple Antiretroviral Therapy Interruptions in People Living With HIV: Comparative Survival Analysis Using Cox and Explainable Machine Learning Models.JMIR medical informatics · 2026Article
- Identification of Clinical Phenotypes Among People with HIV Using Electronic Health Record Data.AIDS and behavior · 2026Article
- AI competency misalignment in preventive medicine: a multi-stakeholder survey with latent profile analysis in Sichuan and Chongqing.International journal of public health · 2026Article
- Impact of a community-based antiretroviral therapy delivery model for people living with HIV in Cambodia.Nature communications · 2025Article
- Comparison of machine learning models to predict loss to follow-up among people with Human Immunodeficiency Virus (HIV).JAMIA open · 2025Article
- Predictive Models to Identify Individuals With HIV at Risk of Unsuppressed Viral Load Using Routine Public Health Data.Journal of acquired immune deficiency syndromes (1999) · 2025Article
- Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.BMC medical informatics and decision making · 2025Article
- Engaging Diverse African American/Black and Latine Youth and Emerging Adults Living with HIV into Research: Description of Recruitment Strategies and Lessons Learned.AIDS and behavior · 2025Article
- Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States.PLOS global public health · 2025Article
- Article
- Predictors of HIV Care Outcomes among Recently Out-of-Care Individuals in The Massachusetts Cooperative Re-engagement Controlled Trial Cohort.AIDS and behavior · 2024Article
- Exploring the Feasibility of an Electronic Tool for Predicting Retention in HIV Care: Provider Perspectives.International journal of environmental research and public health · 2024Article
- Relationships Between Patient Race and Residential Race Context With Missed Human Immunodeficiency Virus Care Visits in the United States, 2010-2015.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2023Article
- Predictive Modeling of Lapses in Care for People Living with HIV in Chicago: Algorithm Development and Interpretation.JMIR public health and surveillance · 2023Article
- Supervised machine learning algorithms to predict the duration and risk of long-term hospitalization in HIV-infected individuals: a retrospective study.Frontiers in public health · 2023Article
- Application of artificial intelligence and machine learning for HIV prevention interventions.The lancet. HIV · 2022Review
Corrections and comments
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
Consistent medical care among people living with HIV is essential for both individual and public health. HIV-positive individuals who are 'retained in care' are more likely to be prescribed antiretroviral medication and achieve HIV viral suppression, effectively eliminating the risk of transmitting HIV to others. However, in the United States, less than half of HIV-positive individuals are retained in care. Interventions to improve retention in care are resource intensive, and there is currently no systematic way to identify patients at risk for falling out of care who would benefit from these interventions. We developed a machine learning model to identify patients at risk for dropping out of care in an urban HIV care clinic using electronic medical records and geospatial data. The machine learning model has a mean positive predictive value of 34.6% [SD: 0.15] for flagging the top 10% highest risk patients as needing interventions, performing better than the previous state-of-the-art logistic regression model (PPV of 17% [SD: 0.06]) and the baseline rate of 11.1% [SD: 0.02]. Machine learning methods can improve the prediction ability in HIV care clinics to proactively identify patients at risk for not returning to medical care.
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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.