Please use this identifier to cite or link to this item: http://cmuir.cmu.ac.th/jspui/handle/6653943832/68424
Title: Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy
Authors: Robert H. Paul
Kyu S. Cho
Andrew C. Belden
Claude A. Mellins
Kathleen M. Malee
Reuben N. Robbins
Lauren E. Salminen
Stephen J. Kerr
Badri Adhikari
Paola M. Garcia-Egan
Jiratchaya Sophonphan
Linda Aurpibul
Kulvadee Thongpibul
Pope Kosalaraksa
Suparat Kanjanavanit
Chaiwat Ngampiyaskul
Jurai Wongsawat
Saphonn Vonthanak
Tulathip Suwanlerk
Victor G. Valcour
Rebecca N. Preston-Campbell
Jacob D. Bolzenious
Merlin L. Robb
Jintanat Ananworanich
Thanyawee Puthanakit
Keywords: Immunology and Microbiology
Medicine
Issue Date: 1-Apr-2020
Abstract: OBJECTIVE: 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.
URI: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081945901&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68424
ISSN: 14735571
Appears in Collections:CMUL: Journal Articles

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