# Obesity Prediction with EHR Data: A deep learning approach with interpretable elements

arXiv: https://arxiv.org/abs/1912.02655 · HTML: https://mlsanity.com/paper/1912.02655/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 15 of 15)

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- [A review of Generative Adversarial Networks for Electronic Health Records: applications, evaluation measures and data sources](https://mlsanity.com/paper/2203.07018/) (2022)
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- [An Interoperable Machine Learning Pipeline for Pediatric Obesity Risk Estimation](https://mlsanity.com/paper/2412.10454/) (2024)
- [HyMaTE: A Hybrid Mamba and Transformer Model for EHR Representation Learning](https://mlsanity.com/paper/2509.24118/) (2025)
- [MOFit: A Framework to reduce Obesity using Machine learning and IoT](https://mlsanity.com/paper/2108.08868/) (2021)
- [Comparison of Missing Data Imputation Methods using the Framingham Heart study dataset](https://mlsanity.com/paper/2210.03154/) (2022)
- [Predicting Visit Cost of Obstructive Sleep Apnea using Electronic Healthcare Records with Transformer](https://mlsanity.com/paper/2301.12289/) (2023)
- [Fairness-Optimized Synthetic EHR Generation for Arbitrary Downstream Predictive Tasks](https://mlsanity.com/paper/2406.02510/) (2024)
- [CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction](https://mlsanity.com/paper/2511.11423/) (2025)
- [Identifying the Leading Factors of Significant Weight Gains Using a New Rule Discovery Method](https://mlsanity.com/paper/2111.04475/) (2021)
- [DeepHealthNet: Adolescent Obesity Prediction System Based on a Deep Learning Framework](https://mlsanity.com/paper/2308.14657/) (2023)
- [Effective Predictive Modeling for Emergency Department Visits and Evaluating Exogenous Variables Impact: Using Explainable Meta-learning Gradient Boosting](https://mlsanity.com/paper/2411.11275/) (2024)
- [Multilevel Determinants of Overweight and Obesity Among U.S. Children Aged 10-17: Comparative Evaluation of Statistical and Machine Learning Approaches Using the 2021 National Survey of Children's Health](https://mlsanity.com/paper/2602.20303/) (2026)

## Abstract (arXiv)

Childhood obesity is a major public health challenge. Early prediction and identification of the children at a high 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 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 medical history. To do this, we use a large unaugmented electronic health records dataset from a large pediatric health system. We adopt a general LSTM network architecture which are known to better represent the longitudinal data. We train our proposed model on both dynamic and static EHR data. Our model is used to predict obesity for ages between 2-20 years. We compared the performance of our LSTM model with other machine learning methods that aggregate over sequential data and ignore temporality. To add interpretability, we have additionally included an attention layer to calculate the attention scores for the timestamps and rank features of each timestamp.