Childhood obesity is a major public health challenge. Obesity in early childhood and adolescence can lead to obesity and other health risks in adulthood. Early prediction and identification of high-risk populations can help to prevent its development. With early identification, proper interventions can be used for its prevention . In this paper, we build prediction models to predict future BMI from baseline medical historydata. We used unaugmented Nemours EHR (Electronic Health Record) data as represented in the PEDSnet (A pediatric Learning Health System) common data model. We trained variety of machine learning models to perform binary classification of obese, and non-obese for children in early childhood ages and during adolescence. We explored if deep learning techniques that can model the temporal nature of EHR datawould improve the performance of predicting obesity as compared to other machine learning techniques that ignore temporality. We also added attention layer at top of rnn layer in our model to compute the  attention scores of each hidden layer corresponding to each input timestep. The attention score for each timestep were computed as an average score given to all the features associated with the timestep. These attention scores added interpretability at both timestep level and the features associated with the timesteps .
Childhood obesity is a major public health challenge. Obesity in early childhood and adolescence can lead to obesity and other health problems in adulthood. 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 this and other related health conditions. Existing predictive tools designed for childhood obesity primarily rely on traditional regression-type methods without exploiting longitudinal patterns of children's data ignoring data temporality . In this paper, we present a machine learning model specifically designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we have used a large unaugmented EHR (Electronic Health Record) dataset from a major pediatric health system in the US. We adopt a general long short-term memory network architecture for our model for training over dynamic (sequential) and static (demographic) EHR data. We have additionally included a set embedding and attention layers to compute the feature ranking of each timestamp and attention scores of each hidden layer corresponding to each input timestamp. These feature ranking and attention scores added interpretability at both the features and the timestamp level .
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risks
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high-risk populations can help to prevent its development. With early identification, proper interventions can be used for its prevention
the children at a high risk of developing childhood obesity may help in engaging earlier and more effective interventions to prevent and manage this and other related health conditions. Existing predictive tools designed for childhood obesity primarily rely on traditional regression-type methods without exploiting longitudinal patterns of children's data ignoring data temporality
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build prediction models to predict future BMI from baseline medical historydata. We used unaugmented Nemours
present a machine learning model specifically designed for predicting future obesity patterns from generally available items on children's medical history. To do this, we have used a large unaugmented
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data as represented in the PEDSnet (A pediatric Learning Health System) common data model. We trained variety of machine learning models to perform binary classification of obese, and non-obese for children in early childhood ages and during adolescence. We explored if deep learning techniques that can model the temporal nature of EHR datawould improve the performance of predicting obesity as compared to other machine learning techniques that ignore temporality. We also added attention layer at top of rnn layer in our model
dataset from a major pediatric health system in the US. We adopt a general long short-term memory network architecture for our model for training over dynamic (sequential) and static (demographic) EHR data. We have additionally included a set embedding and attention layers
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feature ranking of each timestamp and
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timestep. The attention score for each timestep were computed as an average score given to all the features associated with the timestep. These
timestamp. These feature ranking and
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timestep level and the features associated with the timesteps
the features and the timestamp level