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This paper is in the following e-collection/theme issue:

Prevention and Health Promotion (1533) Clinical Information and Decision Making (2149) Tools, Programs and Algorithms (424) Obesity and Nutrition as Public Health Problem (455) Machine Learning (2308) Pregnancy Information, Education and Lifestyle Interventions (431) Infant Feeding Education, Lactation & Breastfeeding Support (51)

Published on in Vol 11 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/69220, first published .
Baby scale, tablet with growth chart, and knitted baby clothes on a desk.

Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

Development of Machine Learning–Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts

Authors of this article:

Miaobing Zheng1, 2 Author Orcid Image ;   Yuxin Zhang1 Author Orcid Image ;   Rachel A Laws1 Author Orcid Image ;   Peter Vuillermin3 Author Orcid Image ;   Jodie Dodd4 Author Orcid Image ;   Li Ming Wen5 Author Orcid Image ;   Louise A Baur5 Author Orcid Image ;   Rachael Taylor6 Author Orcid Image ;   Rebecca Byrne7 Author Orcid Image ;   Anne-Louise Ponsonby8 Author Orcid Image ;   Kylie D Hesketh1 Author Orcid Image
Article Authors Cited by (4) Tweetations Metrics

Journals

  1. Huang L, Chen J. Interpretable machine learning for identifying adolescent obesity risk and identifying key determinants. Frontiers in Public Health 2026;14 View
  2. Shinoda G, Ishikuro M, Matsubara T, Noda A, Murakami K, Orui M, Metoki H, Kikuya M, Hozawa A, Kuriyama S, Nakamura K, Obara T. Routine Life-Course Health Records in Infancy Predict Being Overweight in Childhood and Adolescence: The TMM BirThree Cohort Study. Children 2026;13(3):334 View
  3. Ortega-Ramírez A, Sánchez-Ramírez C, Trujillo-Hernández B, Murillo Zamora E. Predicting rapid weight gain in six-month-old infants: an exploratory modeling study. Pediatric Research 2026 View
  4. Lim J, Park S, Cha T, Yoon S, Han J, Shin J, Song I, Lee S, Eun H, Park M. Machine-Learning Models Outperform Clinicians in Predicting Postnatal Growth Failure Among Very Low Birth Weight Infants. Diagnostics 2026;16(9):1282 View