• DocumentCode
    1375541
  • Title

    AI for Global Disease Surveillance

  • Author

    Hsinchun Chen ; Zeng, Deze

  • Author_Institution
    Univ. of Arizona, Tucson, AZ, USA
  • Volume
    24
  • Issue
    6
  • fYear
    2009
  • Firstpage
    66
  • Lastpage
    82
  • Abstract
    Disease surveillance has been practiced for decades and continues to be an indispensable approach for detecting emerging disease outbreaks and epidemics. Early knowledge of a disease outbreak plays an important role in improving response effectiveness. In this article we presents the application of AI for the global disease surveillance systems. This also provides in depth survey that analyzes and evaluates existing syndromic surveillance system and related outbreak modelling and detection work under a unified framework. Many intelligent global disease surveillance systems have begun to emerge. To illustrate, we present the following study.
  • Keywords
    artificial intelligence; diseases; medical computing; surveillance; AI; artificial intelligence; disease detection; intelligent global disease surveillance systems; syndromic surveillance system; Artificial intelligence; Asia; Bioterrorism; Collaborative work; Diseases; Hurricanes; Laboratories; Monitoring; Surveillance; Time series analysis; Controversies; Trends & bioterrorism; disease surveillance; epidemic monitoring and control; global data sharing; healthcare; literature mining; spatial-contact networks; syndromic surveillance;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2009.126
  • Filename
    5372205