Linking and using social media data for enhancing public health analytics

Xiang Ji, Soon Ae Chun, Paolo Cappellari, James Geller

Research output: Contribution to journalArticlepeer-review

12 Scopus citations

Abstract

There is a large amount of health information available for any patient to address his/her health concerns. The freely available health datasets include community health data at the national, state, and community level, readily accessible and downloadable. These datasets can help to assess and improve healthcare performance, as well as help to modify health-related policies. There are also patient-generated datasets, accessible through social media, on the conditions, treatments, or side effects that individual patients experience. Clinicians and healthcare providers may benefit from being aware of national health trends and individual healthcare experiences that are relevant to their current patients. The available open health datasets vary from structured to highly unstructured. Due to this variability, an information seeker has to spend time visiting many, possibly irrelevant, Websites, and has to select information from each and integrate it into a coherent mental model. In this paper, we discuss an approach to integrating these openly available health data sources and presenting them to be easily understandable by physicians, healthcare staff, and patients. Through linked data principles and Semantic Web technologies we construct a generic model that integrates diverse open health data sources. The integration model is then used as the basis for developing a set of analytics as part of a system called 'Social InfoButtons', providing awareness of both community and patient health issues as well as healthcare trends that may shed light on a specific patient care situation. The prototype system provides patients, public health officials, and healthcare specialists with a unified view of health-related information from both official scientific sources and social networks, and provides the capability of exploring the current data along multiple dimensions, such as time and geographical location.

Original languageEnglish (US)
Pages (from-to)221-245
Number of pages25
JournalJournal of Information Science
Volume43
Issue number2
DOIs
StatePublished - Apr 1 2017

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Library and Information Sciences

Keywords

  • Linked data
  • ontology
  • public health analytics
  • resource description framework (RDF)
  • semantic integration
  • social medical data

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