Deep Learning Models to Predict Pediatric Asthma Emergency Department Visits
This work addresses asthma management for children, but it is incremental as it shows only a minor improvement over an existing method.
This research tackled predicting pediatric asthma emergency department visits within 3 months using Medicaid claims data, finding that an Artificial Neural Network model slightly outperformed a Lasso logistic regression model with AUC scores of 0.845 vs. 0.842.
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.