Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.

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Publication Details

Journal
International Journal of Obesity
Published
2024-11-15
DOI
https://doi.org/10.1038/s41366-024-01679-0
Primary Topic
Birth, Development, and Health
Type
article
Field-Weighted Citation Impact
0.00

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article

Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

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International Journal of Obesity
Birth, Development, and Health
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Prospective prediction of childhood body mass index trajectories using multi-task Gaussian processes

Mauricio A. Álvarez, Yap Seng Chong, Johan G. Eriksson, Arthur Leroy, Keith M. Godfrey, Dennis Wang, Navin Michael, Delicia Shu Qin Ooi, Ngee Lek, Yung Seng Lee, Fabian Yap, Mya Thway Tint, Varsha Gupta
article en

Abstract

MagmaClust provides a unified, probabilistic, non-parametric framework to model, cluster, and prospectively predict childhood BMI trajectories and overweight/obesity risk. The proposed method offers a convenient tool for clinicians to monitor BMI growth in children, allowing them to prospectively identify children with high predicted overweight/obesity risk and implement timely interventions.

International Journal of ObesityVol. 49(2)
Agency for Science, Technology and Research (SG), University of Helsinki (FI), National University of Singapore (SG), University Hospital Southampton NHS Foundation Trust (GB), University of Manchester (GB), Lung Institute (US), Singapore Institute for Clinical Sciences (SG), KK Women's and Children's Hospital (SG), NIHR Southampton Biomedical Research Centre (GB), Duke-NUS Medical School (SG), Bioinformatics Institute (SG), National University Health System (SG), Folkhälsans Forskningscentrum (FI), University of Southampton (GB), Imperial College London (GB), University of Sheffield (GB)
Engineering and Physical Sciences Research Council
Good health and well-being
Openalex Percentile: Top 68%
Birth, Development, and Health
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