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Obesity Prediction with EHR Data: A deep learning approach with interpretable elements
Mehak Gupta1, Thao-Ly T Phan2, H Timothy Bunnell2
1University of Delaware, USA.
Insights
This study introduces a deep learning model for early childhood obesity prediction using electronic health records. The model accurately forecasts obesity risk years in advance, outperforming existing methods.
Area of Science:
- Public Health
- Pediatrics
- Data Science
Background:
- Childhood obesity presents a significant public health concern.
- Early identification of at-risk children is crucial for timely interventions.
- Existing predictive tools often lack the ability to analyze longitudinal data patterns.
Purpose of the Study:
- To develop and evaluate a deep learning model for predicting future childhood obesity.
- To leverage electronic health records (EHR) for predictive modeling.
- To improve upon traditional methods by incorporating longitudinal data analysis.
Main Methods:
- Utilized a large, unaugmented electronic health records dataset from a US pediatric health system.
- Developed a Long Short-Term Memory (LSTM) network architecture incorporating an attention layer.
- Trained the model using both static and dynamic EHR data to predict obesity 1-3 years in advance for ages 3-20.
Main Results:
- The LSTM model demonstrated superior performance in predicting childhood obesity across various age ranges compared to existing literature.
- The attention layer provided interpretability by calculating attention scores for timestamps and ranking features.
- The model effectively predicted future obesity patterns using readily available EHR data.
Conclusions:
- Deep learning, specifically LSTM networks with attention, offers a powerful approach for predicting childhood obesity.
- The proposed model shows promise for early identification and intervention strategies.
- This method enhances predictive accuracy by utilizing longitudinal EHR data, outperforming traditional approaches.
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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