Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy

Machine-learning classification of neurocognitive performance in children with perinatal HIV initiating de novo antiretroviral therapy

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...

وصف كامل

محفوظ في:
التفاصيل البيبلوغرافية
المؤلفون الرئيسيون: 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
التنسيق: دورية
منشور في: 2020
الموضوعات:
Immunology and Microbiology
Medicine
الوصول للمادة أونلاين:https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85081945901&origin=inward
http://cmuir.cmu.ac.th/jspui/handle/6653943832/68424
الوسوم: إضافة وسم
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المؤسسة: Chiang Mai University

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