ReviewNutrients2020
Machine Learning Models to Predict Childhood and Adolescent Obesity: A Review.
Review in Nutrients, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 4 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
62 citing papers in PubMed, 4 syntheses or guidelines pooled it, 128 citations in OpenAlex.
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Risk prediction models for stress urinary incontinence after pelvic organ prolapse (POP) surgery: a systematic review and meta-analysis.BMC women's health · 2025Pooled it
- Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.Journal of medical systems · 2023Pooled it
- Modeling obesity in complex food systems: Systematic review.Frontiers in endocrinology · 2022Pooled it
- Testing an adapted obesity prevention intervention in under resourced schools: a pilot clustered randomized controlled trial.Scientific reports · 2025Trial
- Article
- Identification of weight loss predictors using machine learning approaches in adolescents with obesity.Pediatric research · 2026Article
- Biological and Social Risk Factors for Predicting Type 2 Diabetes in Youth with Prediabetes: Review of Existing Prediction Models.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2026Review
- Artificial Intelligence for Weight Management in Children: A Narrative Review.Healthcare (Basel, Switzerland) · 2026Review
- A prediction model for childhood obesity risk based on maternal thyroid status and related parameters using machine learning: a mother-newborn-offspring study in a mild-to-moderate iodine deficiency area.International journal of obesity (2005) · 2026Article
- Routine Life-Course Health Records in Infancy Predict Being Overweight in Childhood and Adolescence: The TMM BirThree Cohort Study.Children (Basel, Switzerland) · 2026Article
- A Maturation-Aware Machine Learning Framework for Screening the Nutritional Status of Adolescents.Nutrients · 2026Article
- Prediction Models for Early Identification of Overweight and Obese Children-A National Study.Nutrients · 2026Article
- Association between dietary folate intake and obesity in children and adolescents: a cross-sectional NHANES study.BMC pediatrics · 2026Article
- Interpretable machine learning for identifying adolescent obesity risk and identifying key determinants.Frontiers in public health · 2026Article
- Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025Review
- Development of a Deep Learning Model for Predicting Obesity Using Health Behavior Data of Elementary School Students.Iranian journal of public health · 2025Article
- Uncovering age-specific subtypes of pediatric obesity and metabolic syndrome using machine learning algorithms.Scientific reports · 2025Article
- Evaluating algorithmic fairness of machine learning models in predicting underweight, overweight, and adiposity across socioeconomic and caste groups in India: evidence from the longitudinal ageing study in India.PLOS digital health · 2025Article
- Neighborhood Environmental and Contextual Factors Improve Prediction of Childhood Body Mass Index: An Application of Novel Graph Neural Networks.AJE advances : research in epidemiology · 2025Article
2 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The prevalence of childhood and adolescence overweight an obesity is raising at an alarming rate in many countries. This poses a serious threat to the current and near-future health systems, given the association of these conditions with different comorbidities (cardiovascular diseases, type II diabetes, and metabolic syndrome) and even death. In order to design appropriate strategies for its prevention, as well as understand its origins, the development of predictive models for childhood/adolescent overweight/obesity and related outcomes is of extreme value. Obesity has a complex etiology, and in the case of childhood and adolescence obesity, this etiology includes also specific factors like (pre)-gestational ones; weaning; and the huge anthropometric, metabolic, and hormonal changes that during this period the body suffers. In this way, Machine Learning models are becoming extremely useful tools in this area, given their excellent predictive power; ability to model complex, nonlinear relationships between variables; and capacity to deal with high-dimensional data typical in this area. This is especially important given the recent appearance of large repositories of Electronic Health Records (EHR) that allow the development of models using datasets with many instances and predictor variables, from which Deep Learning variants can generate extremely accurate predictions. In the current work, the area of Machine Learning models to predict childhood and adolescent obesity and related outcomes is comprehensively and critically reviewed, including the latest ones using Deep Learning with EHR. These models are compared with the traditional statistical ones that used mainly logistic regression. The main features and applications appearing from these models are described, and the future opportunities are discussed.
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What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.