2019/07/25 by Xiao Wang, Wang, Xiao, Zhijie Wang +7
Computer Science · Medicine · #Applications (stat.AP) #Chronic Obstructive Pulmonary Disease (COPD) Research #Emergency and Acute Care Studies #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare
paper · pdf · doi:10.48550/arxiv.1907.11195
openalex publication_date 2019/07/25 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28
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.