Before revision:  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.
After revision:  We used unaugmented Nemours 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 attention scores of each hidden layer corresponding to each input timestep.