ArticlePLOS global public health2022
Machine learning with routine electronic medical record data to identify people at high risk of disengagement from HIV care in Tanzania.
Article in PLOS global public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 2 of them syntheses that pooled it.
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The trial behind it
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Who cites it
16 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled 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
- Enhanced language models for predicting and understanding HIV care disengagement: a case study in Tanzania.NPJ digital medicine · 2026Article
- Machine learning risk stratification to identify people living with HIV at high risk of delayed ART and advanced immunosuppression: a precision public health approach.Frontiers in public health · 2026Article
- Applying Machine Learning to Predict Loss to Follow-Up Among People Living With HIV in Haiti Using a National Electronic Medical Record Cohort.International journal of public health · 2026Article
- Development and Validation of a Novel Risk Calculator to Predict Sub-optimal HIV Outcomes Among Pregnant and Postpartum Women with HIV in Kenya.AIDS and behavior · 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
- Machine learning to improve HIV screening using routine data in Kenya.Journal of the International AIDS Society · 2025Article
- Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States.PLOS global public health · 2025Article
- Predicting and identifying correlates of inequalities in breast cancer screening uptake using national level data from India.Frontiers in artificial intelligence · 2025Article
- Very high HIV prevalence and incidence among men who have sex with men and transgender women in Indonesia: a retrospective observational cohort study in Bali and Jakarta, 2017-2020.Journal of the International AIDS Society · 2024Observational
- Observational
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- Historical visit attendance as predictor of treatment interruption in South African HIV patients: Extension of a validated machine learning model.PLOS global public health · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
Abstract
Machine learning methods for health care delivery optimization have the potential to improve retention in HIV care, a critical target of global efforts to end the epidemic. However, these methods have not been widely applied to medical record data in low- and middle-income countries. We used an ensemble decision tree approach to predict risk of disengagement from HIV care (missing an appointment by ≥28 days) in Tanzania. Our approach used routine electronic medical records (EMR) from the time of antiretroviral therapy (ART) initiation through 24 months of follow-up for 178 adults (63% female). We compared prediction accuracy when using EMR-based predictors alone and in combination with sociodemographic survey data collected by a research study. Models that included only EMR-based indicators and incorporated changes across past clinical visits achieved a mean accuracy of 75.2% for predicting risk of disengagement in the next 6 months, with a mean sensitivity of 54.7% for targeting the 30% highest-risk individuals. Additionally including survey-based predictors only modestly improved model performance. The most important variables for prediction were time-varying EMR indicators including changes in treatment status, body weight, and WHO clinical stage. Machine learning methods applied to existing EMR data in resource-constrained settings can predict individuals' future risk of disengagement from HIV care, potentially enabling better targeting and efficiency of interventions to promote retention in care.
Identifiers
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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.