Open source text based biovigilance

Madhav Erraguntla, Larissa S May, Belita Gopal, Richard J. Mayer, Perakath C. Benjamin

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

1 Scopus citations


Timely detection of disease outbreak events is of paramount importance for the defense against infectious diseases and biological threats. Internet-based communications can provide good situational awareness for countries where public data collection is inadequate, unreliable or missing. The key challenge is to sift through this vast amount of unstructured text to identify relevant reports and to extract disease related information into a structured format suitable for analysis. In this work, Natural Language Processing (NLP) techniques are used on data from news feeds, websites, and medical publications to extract key biological event data. We developed the Threat Assessment Dashboard (BioTHAD™) in order to improve detection and monitoring of biological events. We demonstrate that disease outbreak incidence and timing can be effectively extracted from open news sources using NLP. The BioTHAD™ application could serve as a model for tracking not only infectious, but chronic diseases and other types of events worldwide.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 International Conference on Artificial Intelligence, ICAI 2012
Number of pages6
StatePublished - 2012
Externally publishedYes
Event2012 International Conference on Artificial Intelligence, ICAI 2012 - Las Vegas, NV, United States
Duration: Jul 16 2012Jul 19 2012


Other2012 International Conference on Artificial Intelligence, ICAI 2012
Country/TerritoryUnited States
CityLas Vegas, NV


  • Biovigilance
  • Disease outbreaks
  • Natural language processing
  • Open sources based surveillance
  • Text mining

ASJC Scopus subject areas

  • Artificial Intelligence


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