ArticleJournal of the American Medical Informatics Association : JAMIA2021
Development of a predictive model for retention in HIV care using natural language processing of clinical notes.
Article in Journal of the American Medical Informatics Association : JAMIA, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 1 of them a synthesis that pooled it.
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Who cites it
14 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Trial
- Towards Accurate and Reliable ICU Outcome Prediction: A Multimodal Learning Framework Based on Belief Function Theory using Structured EHRs and Free-Text Notes.Journal of healthcare informatics research · 2026Article
- Identification of Clinical Phenotypes Among People with HIV Using Electronic Health Record Data.AIDS and behavior · 2026Article
- A turning point for youth HIV care in the United States: tailored strategies for adolescents and young adults.Critical public health · 2026Article
- Artificial intelligence in HIV research: a structured review and task-oriented clinical framework.Frontiers in digital health · 2026Review
- 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
- Article
- Demystifying the Role of Natural Language Processing (NLP) in Smart City Applications: Background, Motivation, Recent Advances, and Future Research Directions.Wireless personal communications · 2023Review
- Emergence and evolution of big data science in HIV research: Bibliometric analysis of federally sponsored studies 2000-2019.International journal of medical informatics · 2021Review
- Machine Learning and Clinical Informatics for Improving HIV Care Continuum Outcomes.Current HIV/AIDS reports · 2021Review
- UbiComb: A Hybrid Deep Learning Model for Predicting Plant-Specific Protein Ubiquitylation Sites.Genes · 2021Article
- Natural Language Processing of Clinical Notes to Identify Mental Illness and Substance Use Among People Living with HIV: Retrospective Cohort Study.JMIR medical informatics · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
objectiveAdherence to a treatment plan from HIV-positive patients is necessary to decrease their mortality and improve their quality of life, however some patients display poor appointment adherence and become lost to follow-up (LTFU). We applied natural language processing (NLP) to analyze indications towards or against LTFU in HIV-positive patients' notes. MATERIALS AND
methodsUnstructured lemmatized notes were labeled with an LTFU or Retained status using a 183-day threshold. An NLP and supervised machine learning system with a linear model and elastic net regularization was trained to predict this status. Prevalence of characteristics domains in the learned model weights were evaluated.
resultsWe analyzed 838 LTFU vs 2964 Retained notes and obtained a weighted F1 mean of 0.912 via nested cross-validation; another experiment with notes from the same patients in both classes showed substantially lower metrics. "Comorbidities" were associated with LTFU through, for instance, "HCV" (hepatitis C virus) and likewise "Good adherence" with Retained, represented with "Well on ART" (antiretroviral therapy). DISCUSSION: Mentions of mental health disorders and substance use were associated with disparate retention outcomes, however history vs active use was not investigated. There remains further need to model transitions between LTFU and being retained in care over time.
conclusionWe provided an important step for the future development of a model that could eventually help to identify patients who are at risk for falling out of care and to analyze which characteristics could be factors for this. Further research is needed to enhance this method with structured electronic medical record fields.
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Registered trials
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