ArticleJournal of acquired immune deficiency syndromes (1999)2020
Machine Learning Analysis Reveals Novel Neuroimaging and Clinical Signatures of Frailty in HIV.
Article in Journal of acquired immune deficiency syndromes (1999), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers, 2 of them syntheses that pooled it.
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Who cites it
20 citing papers in PubMed, 2 syntheses or guidelines pooled it, 29 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
- Predictive and Explainable Artificial Intelligence for Neuroimaging Applications.Diagnostics (Basel, Switzerland) · 2024Review
- Mental health phenotypes of well-controlled HIV in Uganda.Frontiers in public health · 2024Article
- Biopsychosocial phenotypes in people with HIV in the CHARTER cohort.Brain communications · 2024Article
- Comparison of Resting State Functional Connectivity in Persons With and Without HIV: A Cross-sectional Study.The Journal of infectious diseases · 2023Article
- Protocol for a randomized controlled trial in Cambodian individuals with PTSD: Trauma-Informed Treatment Algorithms for Advancing Novel Outcomes (Project TITAN).Contemporary clinical trials · 2023Article
- 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
- The Structural and Functional Correlates of Frailty in Persons With Human Immunodeficiency Virus.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2022Article
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- Co-Infection and Cancer: Host-Pathogen Interaction between Dendritic Cells and HIV-1, HTLV-1, and Other Oncogenic Viruses.Viruses · 2022Review
- Using machine learning approaches to predict timely clinic attendance and the uptake of HIV/STI testing post clinic reminder messages.Scientific reports · 2022Article
- Resting state network mapping in individuals using deep learning.Frontiers in neurology · 2022Article
- Multi-label, multi-domain learning identifies compounding effects of HIV and cognitive impairment.Medical image analysis · 2022Article
- Neuroimaging the Neuropathogenesis of HIV.Current HIV/AIDS reports · 2021Review
- Ensemble machine learning classification of daily living abilities among older people with HIV.EClinicalMedicine · 2021Article
- Neurocognitive Trajectories After 72 Weeks of First-Line Anti-retroviral Therapy in Vietnamese Adults With HIV-HCV Co-infection.Frontiers in neurology · 2021Article
- Machine Learning Classification of False-Positive Human Immunodeficiency Virus Screening Results.Journal of pathology informatics · 2021Article
Corrections and comments
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Authors and funding
15 authors at 4 institutions in 2 countries.
Funding
Abstract
backgroundFrailty is an important clinical concern for the aging population of people living with HIV (PLWH). The objective of this study was to identify the combination of risk features that distinguish frail from nonfrail individuals.
settingMachine learning analysis of highly dimensional risk features was performed on a clinical cohort of PLWH.
methodsParticipants included 105 older (average age = 55.6) PLWH, with at least a 3-month history of combination antiretroviral therapy (median CD4 = 546). Predictors included demographics, HIV clinical markers, comorbid health conditions, cognition, and neuroimaging (ie, volumetrics, resting-state functional connectivity, and cerebral blood flow). Gradient-boosted multivariate regressions were implemented to establish linear and interactive classification models. Model performance was determined by sensitivity/specificity (F1 score) with 5-fold cross validation.
resultsThe linear gradient-boosted multivariate regression classifier included lower current CD4 count, lower psychomotor performance, and multiple neuroimaging indices (volumes, network connectivity, and blood flow) in visual and motor brain systems (F1 score = 71%; precision = 84%; and sensitivity = 66%). The interactive model identified novel synergies between neuroimaging features, female sex, symptoms of depression, and current CD4 count.
conclusionsData-driven algorithms built from highly dimensional clinical and brain imaging features implicate disruption to the visuomotor system in older PLWH designated as frail individuals. Interactions between lower CD4 count, female sex, depressive symptoms, and neuroimaging features suggest potentiation of risk mechanisms. Longitudinal data-driven studies are needed to guide clinical strategies capable of preventing the development of frailty as PLWH reach advanced age.
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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.