title: Predicting asthma-related emergency department visits using big data.
abstract: Asthma is one of the most prevalent and costly chronic conditions in the United States, which cannot be cured. However, accurate and timely surveillance data could allow for timely and targeted interventions at the community or individual level. Current national asthma disease surveillance systems can have data availability lags of up to two weeks. Rapid progress has been made in gathering nontraditional, digital information to perform disease surveillance. We introduce a novel method of using multiple data sources for predicting the number of asthma-related emergency department (ED) visits in a specific area. Twitter data, Google search interests, and environmental sensor data were collected for this purpose. Our preliminary findings show that our model can predict the number of asthma ED visits based on near-real-time environmental and social media data with approximately 70% precision. The results can be helpful for public health surveillance, ED preparedness, and targeted patient interventions.
authors.forename: Sudha
authors.initials: S
authors.lastname: Ram
authors.forename: Wenli
authors.initials: W
authors.lastname: Zhang
authors.forename: Max
authors.initials: M
authors.lastname: Williams
authors.forename: Yolande
authors.initials: Y
authors.lastname: Pengetnze
mesh_terms: D000397:Air Pollution; D001249:Asthma / Q000453:epidemiology*; D016208:Databases, Factual; D003663:Decision Trees; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006801:Humans; D008492:Medical Informatics Computing; D015233:Models, Statistical; D016571:Neural Networks, Computer; D061108:Social Media