ReviewCurrent HIV/AIDS reports2021
Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.
Review in Current HIV/AIDS reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled 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
- AI for Prognosis Among People Living With HIV: Protocol for a Systematic Review and Meta-Analysis.JMIR research protocols · 2026Article
- Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care.Journal of medical Internet research · 2026Article
- Artificial intelligence in HIV research, policy, and clinical care.The lancet. HIV · 2026Review
- 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
- Article
- 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
- Comparison of machine learning models to predict loss to follow-up among people with Human Immunodeficiency Virus (HIV).JAMIA open · 2025Article
- Predicting low density lipoprotein cholesterol target attainment using machine learning in patients with coronary artery disease receiving moderate-dose statin therapy.Scientific reports · 2025Article
- Developing a Prototype Machine Learning Model to Predict Quality of Life Measures in People Living With HIV.Integrated pharmacy research & practice · 2025Article
- Multilingual voice-enabled informatics tools: Catalyst for equitable AI in HIV and HIV-comorbidity healthcare management.PloS one · 2025Article
- Use of machine learning in predicting continuity of HIV treatment in selected Nigerian States.PLOS global public health · 2025Article
- Pediatric Intensive Care Unit Length of Stay Prediction by Machine Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Assessing Patient Acceptance of an Automated Algorithm to Identify Ciswomen for HIV Pre-Exposure Prophylaxis.Journal of women's health (2002) · 2024Article
- Article
- The NeoRoo mobile app: Initial design and prototyping of an Android-based digital health tool to support Kangaroo Mother Care in low/middle-income countries (LMICs).PLOS digital health · 2023Article
- Progress of the Gulf Cooperation Council (GCC) Countries Towards Achieving the 95-95-95 UNAIDS Targets: A Review.Journal of epidemiology and global health · 2023Review
- Transforming clinical virology with AI, machine learning and deep learning: a comprehensive review and outlook.Virusdisease · 2023Review
- HIV testing in a high prevalence urban area in the US: Identifying missed opportunities two ways.International journal of STD & AIDS · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
purpose of reviewThis manuscript reviews the use of electronic medical record (EMR) data for HIV care and research along the HIV care continuum with a specific focus on machine learning methods and clinical informatics interventions. RECENT
findingsEMR-based clinical decision support tools and electronic alerts have been effectively utilized to improve HIV care continuum outcomes. Accurate EMR-based machine learning models have been developed to predict HIV diagnosis, retention in care, and viral suppression. Natural language processing (NLP) of clinical notes and data sharing between healthcare systems and public health agencies can enhance models for identifying people living with HIV who are undiagnosed or in need of relinkage to care. Challenges related to using these technologies include inconsistent EMR documentation, alert fatigue, and the potential for bias. Clinical informatics and machine learning models are promising tools for improving HIV care continuum outcomes. Future research should focus on methods for combining EMR data with additional data sources (e.g., social media, geospatial data) and studying how to effectively implement predictive models for HIV care into clinical practice.
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What OpenQuestion holds
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.