Extracting signals from social media for chronic disease surveillance

Wenli Zhang, Sudha Ram, Mark Burkart, Yolande Pengetnze

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Scopus citations

Abstract

Asthma is a chronic disease that affects people of all ages, and is a serious health and economic concern worldwide. However, accurate and timely surveillance and predicting hospital visits could allow for targeted interventions and reduce the societal burden of asthma. Current national asthma disease surveillance systems can have data availability lags of up to months and years. Rapid progress has been made in gathering social media data to perform disease surveillance and prediction. We introduce novel methods for extracting signals from social media data to assist in accurate and timely asthma surveillance. Our empirical analyses show that our methods are very effective for surveillance of asthma prevalence at both state and municipal levels. They are also useful for predicting the number of hospital visits based on near-real-Time social media data for specific geographic areas. Our results can be used for public health surveillance, ED preparedness, and targeted patient interventions.

Original languageEnglish (US)
Title of host publicationDH 2016 - Proceedings of the 2016 Digital Health Conference
PublisherAssociation for Computing Machinery, Inc
Pages79-83
Number of pages5
ISBN (Print)9781450342247
DOIs
StatePublished - Apr 11 2016
Event6th International Conference on Digital Health, DH 2016 - Montreal, Canada
Duration: Apr 11 2016Apr 13 2016

Other

Other6th International Conference on Digital Health, DH 2016
CountryCanada
CityMontreal
Period4/11/164/13/16

Keywords

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

ASJC Scopus subject areas

  • Health Information Management
  • Computer Science Applications
  • Health Informatics

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  • Cite this

    Zhang, W., Ram, S., Burkart, M., & Pengetnze, Y. (2016). Extracting signals from social media for chronic disease surveillance. In DH 2016 - Proceedings of the 2016 Digital Health Conference (pp. 79-83). Association for Computing Machinery, Inc. https://doi.org/10.1145/2896338.2896340