  Pediatric asthma is the most prevalent chronic childhood illness, afflicting
about 6.2 million children in the United States. However, asthma could be
better managed by identifying and avoiding triggers, educating about
medications and proper disease management strategies. This research utilizes
deep learning methodologies to predict asthma-related emergency department (ED)
visit within 3 months using Medicaid claims data. We compare prediction results
against traditional statistical classification model - penalized Lasso logistic
regression, which we trained and have deployed since 2015. The results have
indicated that deep learning model Artificial Neural Networks (ANN) slightly
outperforms (with AUC = 0.845) the Lasso logistic regression (with AUC =
0.842). The reason may come from the nonlinear nature of ANN.
