ArticleNPJ digital medicine2024
A machine-learned model for predicting weight loss success using weight change features early in treatment.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Machine learning algorithms for predicting glycemic control and weight loss outcomes in GLP-1 receptor agonist users.Frontiers in artificial intelligence · 2026Article
- Domain-informed weight forecasting: leveraging behavioral and physiological sequences from wearables.Frontiers in public health · 2026Article
- An interpretable prediction model for non-invasive identification of the phlegm-dampness constitution based on machine learning.Frontiers in endocrinology · 2026Article
- Forest-EMCBE: an evolutionary ensemble learning algorithm for multiclass diagnosis of bacterial pneumonia using the CBC dataset.Frontiers in bioinformatics · 2026Article
- Multimodal and Multidimensional Artificial Intelligence Technology in Obesity.Journal of obesity & metabolic syndrome · 2025Review
- Predictive Modeling of Weight Loss and Metabolic Health Outcomes: A Retrospective Predictive Modeling Study.Health science reports · 2025Article
- Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence.Frontiers in physiology · 2025Article
- Article
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Authors and funding
6 authors.
Funding
Abstract
Stepped-care obesity treatments aim to improve efficiency by early identification of non-responders and adjusting interventions but lack validated models. We trained a random forest classifier to improve the predictive utility of a clinical decision rule (>0.5 lb weight loss/week) that identifies non-responders in the first 2 weeks of a stepped-care weight loss trial (SMART). From 2009 to 2021, 1058 individuals with obesity participated in three studies: SMART, Opt-IN, and ENGAGED. The model was trained on 80% of the SMART data (224 participants), and its in-distribution generalizability was tested on the remaining 20% (remaining 57 participants). The out-of-distribution generalizability was tested on the ENGAGED and Opt-IN studies (472 participants). The model predicted weight loss at month 6 with an 84.5% AUROC and an 86.3% AUPRC. SHAP identified predictive features: weight loss at week 2, ranges/means and ranges of weight loss, slope, and age. The SMART-trained model showed generalizable performance with no substantial difference across studies.
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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 OpenQuestion graph.