Predicting Asthma-Related Emergency Department Visits Using Big Data

Sudha Ram, Wenli Zhang, Max Williams, Yolande Pengetnze

Research output: Contribution to journalArticle

84 Scopus citations

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.

Original languageEnglish (US)
Article number7045443
Pages (from-to)1216-1223
Number of pages8
JournalIEEE Journal of Biomedical and Health Informatics
Volume19
Issue number4
DOIs
StatePublished - Jul 1 2015

Keywords

  • Asthma
  • Big Data
  • Emergency Department Visits
  • Environmental Sensors
  • Predictive Modeling

ASJC Scopus subject areas

  • Biotechnology
  • Computer Science Applications
  • Electrical and Electronic Engineering
  • Health Information Management

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