ArticleACM transactions on computing for healthcare2022
Obesity Prediction with EHR Data: A deep learning approach with interpretable elements.
Article in ACM transactions on computing for healthcare, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers, 2 of them syntheses that pooled it.
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Who cites it
31 citing papers in PubMed, 2 syntheses or guidelines pooled it, 95 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
- Enhancing Patient Outcome Prediction Through Deep Learning With Sequential Diagnosis Codes From Structured Electronic Health Record Data: Systematic Review.Journal of medical Internet research · 2025Pooled it
- CURENet: combining unified representations for efficient chronic disease prediction.Health information science and systems · 2026Article
- Artificial Intelligence-Based Risk Stratification in Obesity Care: From Diagnosis to Personalised Treatment Pathways.Diagnostics (Basel, Switzerland) · 2026Review
- A multimodal, risk-stratified framework for AI-driven early risk prediction and personalised prevention in obesity.Frontiers in artificial intelligence · 2026Article
- Digital twin-supported behavioral intention in mothers of young children to prevent childhood obesity: a large language model-based intervention study.Frontiers in artificial intelligence · 2026Article
- Interpretable machine learning for identifying adolescent obesity risk and identifying key determinants.Frontiers in public health · 2026Article
- Development of a Deep Learning Model for Predicting Obesity Using Health Behavior Data of Elementary School Students.Iranian journal of public health · 2025Article
- The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.Medicina (Kaunas, Lithuania) · 2025Review
- 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
- Spatiotemporal prediction of obesity rates and model interpretability analysis from a public health perspective.PloS one · 2025Article
- Multi-Modal Fusion of Routine Care Electronic Health Records (EHR): A Scoping Review.Information (Basel) · 2025Article
- Human intention recognition for trauma resuscitation: An interpretable deep learning approach for medical process data.Journal of biomedical informatics · 2025Article
- A Review on Deep Learning for Quality of Life Assessment Through the Use of Wearable Data.IEEE open journal of engineering in medicine and biology · 2025Article
- Reliable prediction of childhood obesity using only routinely collected EHRs may be possible.Obesity pillars · 2024Article
- An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation.Proceedings of machine learning research · 2024Article
- Unraveling the Potential of Attentive Bi-LSTM for Accurate Obesity Prognosis: Advancing Public Health towards Sustainable Cities.Bioengineering (Basel, Switzerland) · 2024Article
- Prediction of adolescent weight status by machine learning: a population-based study.BMC public health · 2024Article
- Article
- Robust identification key predictors of short- and long-term weight status in children and adolescents by machine learning.Frontiers in public health · 2024Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
Childhood obesity is a major public health challenge. Early prediction and identification of the children at an elevated risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage obesity. Most existing predictive tools for childhood obesity primarily rely on traditional regression-type methods using only a few hand-picked features and without exploiting longitudinal patterns of children's data. Deep learning methods allow the use of high-dimensional longitudinal datasets. In this paper, we present a deep learning model designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system in the US. We adopt a general LSTM network architecture and train our proposed model using both static and dynamic EHR data. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp. Our model is used to predict obesity for ages between 3-20 years using the data from 1-3 years in advance. We compare the performance of our LSTM model with a series of existing studies in the literature and show it outperforms their performance in most age ranges.
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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.