Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Deep Learning Models to Predict Pediatric Asthma Emergency Department
  Visits   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.

8
The categories of this paper are: hep-ex
37
The categories of this paper are: supr-con
22
The categories of this paper are: chao-dyn
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
25
The categories of this paper are: patt-sol
19
The categories of this paper are: eess
35
The categories of this paper are: mtrl-th
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
31
The categories of this paper are: ao-sci
33
The categories of this paper are: plasm-ph
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
30
The categories of this paper are: q-alg
13
The categories of this paper are: quant-ph