ArticleAIDS (London, England)2020
Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy.
Article in AIDS (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 20 citations in OpenAlex.
- Machine Learning in HIV Care and Antiretroviral Therapy: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence for HIV care: a global systematic review of current studies and emerging trends.Journal of the International AIDS Society · 2025Pooled it
- Cognitive Functioning and Its Stability Among Young Adults with Perinatal HIV Exposure or Acquisition in a U.S.-Based Longitudinal Cohort Study.AIDS and behavior · 2026Article
- AI Methods Tailored to Influenza, RSV, HIV, and SARS-CoV-2: A Focused Review.Pathogens (Basel, Switzerland) · 2025Review
- Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.Scientific reports · 2025Article
- AI applications in HIV research: advances and future directions.Frontiers in microbiology · 2025Review
- Mental health phenotypes of well-controlled HIV in Uganda.Frontiers in public health · 2024Article
- Machine Learning Approaches to Understand Cognitive Phenotypes in People With HIV.The Journal of infectious diseases · 2023Article
- Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals.International journal of general medicine · 2023Article
- Cognitive Phenotypes of HIV Defined Using a Novel Data-driven Approach.Journal of neuroimmune pharmacology : the official journal of the Society on NeuroImmune Pharmacology · 2022Article
- Adaptation and construct validity evaluation of a tablet-based, short neuropsychological test battery for use with adolescents and young adults living with HIV in Thailand.Neuropsychology · 2022Article
- Central Nervous System Impact of Perinatally Acquired HIV in Adolescents and Adults: an Update.Current HIV/AIDS reports · 2022Review
- Identifying influential neighbors in social networks and venue affiliations among young MSM: a data science approach to predict HIV infection.AIDS (London, England) · 2021Article
- Ensemble machine learning classification of daily living abilities among older people with HIV.EClinicalMedicine · 2021Article
- Designing a Predictive Model for Antiretroviral Regimen at the Antiretroviral Therapy Center in Chiro Hospital, Ethiopia.Journal of healthcare engineering · 2021Article
- State of the Mind: Growing up with HIV.Paediatric drugs · 2020Review
- Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.Journal of acquired immune deficiency syndromes (1999) · 2020Article
Corrections and comments
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Authors and funding
26 authors at 16 institutions in 4 countries.
Funding
Abstract
objectiveTo develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV).
designMachine learning analysis of baseline and longitudinal predictors derived from clinical measures utilized in pediatric HIV.
methodsTwo hundred and eighty-five children (ages 2-14 years at baseline; Mage = 6.4 years) with pHIV in Southeast Asia underwent neurocognitive assessment at study enrollment and twice annually thereafter for an average of 5.4 years. Neurocognitive slopes were modeled to establish two subgroups [above (n = 145) and below average (n = 140) trajectories). Gradient-boosted multivariate regressions (GBM) with five-fold cross validation were conducted to examine baseline (pre-ART) and longitudinal predictive features derived from demographic, HIV disease, immune, mental health, and physical health indices (i.e. complete blood count [CBC]).
resultsThe baseline GBM established a classifier of neurocognitive group designation with an average AUC of 79% built from HIV disease severity and immune markers. GBM analysis of longitudinal predictors with and without interactions improved the average AUC to 87 and 90%, respectively. Mental health problems and hematocrit levels also emerged as salient features in the longitudinal models, with novel interactions between mental health problems and both CD4 cell count and hematocrit levels. Average AUCs derived from each GBM model were higher than results obtained using logistic regression.
conclusionOur findings support the feasibility of machine learning to identify children with pHIV at risk for suboptimal neurocognitive development. Results also suggest that interactions between HIV disease and mental health problems are early antecedents to neurocognitive difficulties in later childhood among youth with pHIV.
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