%0 Journal Article
%@ 1438-8871
%I JMIR Publications
%V 26
%N 
%P e50652
%T Using Longitudinal Twitter Data for Digital Epidemiology of Childhood Health Outcomes: An Annotated Data Set and Deep Neural Network Classifiers
%A Klein,Ari Z
%A Gutiérrez Gómez,José Agustín
%A Levine,Lisa D
%A Gonzalez-Hernandez,Graciela
%+ Department of Computational Biomedicine, Cedars-Sinai Medical Center, Pacific Design Center, Ste G549F, 700 N San Vicente Blvd, West Hollywood, CA, 90069, United States, 1 310 423 3521, Graciela.GonzalezHernandez@csmc.edu
%K natural language processing
%K machine learning
%K data mining
%K social media
%K Twitter
%K pregnancy
%K epidemiology
%K developmental disabilities
%K asthma
%D 2024
%7 25.3.2024
%9 Research Letter
%J J Med Internet Res
%G English
%X We manually annotated 9734 tweets that were posted by users who reported their pregnancy on Twitter, and used them to train, evaluate, and deploy deep neural network classifiers (F1-score=0.93) to detect tweets that report having a child with attention-deficit/hyperactivity disorder (678 users), autism spectrum disorders (1744 users), delayed speech (902 users), or asthma (1255 users), demonstrating the potential of Twitter as a complementary resource for assessing associations between pregnancy exposures and childhood health outcomes on a large scale.
%M 38526542
%R 10.2196/50652
%U https://www.jmir.org/2024/1/e50652
%U https://doi.org/10.2196/50652
%U http://www.ncbi.nlm.nih.gov/pubmed/38526542