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Childhood obesity is a major public health challenge. Early prediction andidentification of the children at a high risk of developing childhood obesitymay help in engaging earlier and more effective interventions to prevent andmanage obesity. Most existing predictive tools for childhood obesity primarilyrely on traditional regression-type methods using only a few hand-pickedfeatures and without exploiting longitudinal patterns of children data. Deeplearning methods allow the use of high-dimensional longitudinal datasets. Inthis paper, we present a deep learning model designed for predicting futureobesity patterns from generally available items on children medical history. Todo this, we use a large unaugmented electronic health records dataset from alarge pediatric health system. We adopt a general LSTM network architecturewhich are known to better represent the longitudinal data. We train ourproposed model on both dynamic and static EHR data. Our model is used topredict obesity for ages between 2-20 years. We compared the performance of ourLSTM model with other machine learning methods that aggregate over sequentialdata and ignore temporality. To add interpretability, we have additionallyincluded an attention layer to calculate the attention scores for thetimestamps and rank features of each timestamp.

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