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dc.contributor.authorRobert H. Paulen_US
dc.contributor.authorKyu S. Choen_US
dc.contributor.authorAndrew C. Beldenen_US
dc.contributor.authorClaude A. Mellinsen_US
dc.contributor.authorKathleen M. Maleeen_US
dc.contributor.authorReuben N. Robbinsen_US
dc.contributor.authorLauren E. Salminenen_US
dc.contributor.authorStephen J. Kerren_US
dc.contributor.authorBadri Adhikarien_US
dc.contributor.authorPaola M. Garcia-Eganen_US
dc.contributor.authorJiratchaya Sophonphanen_US
dc.contributor.authorLinda Aurpibulen_US
dc.contributor.authorKulvadee Thongpibulen_US
dc.contributor.authorPope Kosalaraksaen_US
dc.contributor.authorSuparat Kanjanavaniten_US
dc.contributor.authorChaiwat Ngampiyaskulen_US
dc.contributor.authorJurai Wongsawaten_US
dc.contributor.authorSaphonn Vonthanaken_US
dc.contributor.authorTulathip Suwanlerken_US
dc.contributor.authorVictor G. Valcouren_US
dc.contributor.authorRebecca N. Preston-Campbellen_US
dc.contributor.authorJacob D. Bolzeniousen_US
dc.contributor.authorMerlin L. Robben_US
dc.contributor.authorJintanat Ananworanichen_US
dc.contributor.authorThanyawee Puthanakiten_US
dc.description.abstractOBJECTIVE: To develop a predictive model of neurocognitive trajectories in children with perinatal HIV (pHIV). DESIGN: Machine learning analysis of baseline and longitudinal predictors derived from clinical measures utilized in pediatric HIV. METHODS: Two 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]). RESULTS: The 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. CONCLUSION: Our 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.en_US
dc.subjectImmunology and Microbiologyen_US
dc.titleMachine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapyen_US
article.title.sourcetitleAIDS (London, England)en_US
article.volume34en_US Red Cross Agencyen_US Hospitalen_US of Missouri-St. Louisen_US York State Psychiatric Instituteen_US of Southern Californiaen_US Hospitalen_US Universityen_US of California, San Franciscoen_US Kaen Universityen_US University Feinberg School of Medicineen_US Institute of Mental Healthen_US van Amsterdamen_US Mai Universityen_US Institute for Health Sciencesen_US of Health Sciencesen_US - The Foundation for AIDS Researchen_US Infectious Diseases Instituteen_US
Appears in Collections:CMUL: Journal Articles

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