Electronic Healthcare. Third International Conference, eHealth 2010, Casablanca, Morocco, December 13-15, 2010, Revised Selected Papers

Research Article

Detecting Public Health Indicators from the Web for Epidemic Intelligence

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  • @INPROCEEDINGS{10.1007/978-3-642-23635-8_2,
        author={Avar\^{e} Stewart and Marco Fisichella and Kerstin Denecke},
        title={Detecting Public Health Indicators from the Web for Epidemic Intelligence},
        proceedings={Electronic Healthcare. Third International Conference, eHealth 2010, Casablanca, Morocco, December 13-15, 2010, Revised Selected Papers},
        proceedings_a={E-HEALTH},
        year={2012},
        month={10},
        keywords={Epidemic Intelligence Surveillance and Analysis},
        doi={10.1007/978-3-642-23635-8_2}
    }
    
  • Avaré Stewart
    Marco Fisichella
    Kerstin Denecke
    Year: 2012
    Detecting Public Health Indicators from the Web for Epidemic Intelligence
    E-HEALTH
    Springer
    DOI: 10.1007/978-3-642-23635-8_2
Avaré Stewart1,*, Marco Fisichella1,*, Kerstin Denecke1,*
  • 1: L3S Research Center
*Contact email: stewart@L3S.de, fisichella@L3S.de, denecke@L3S.de

Abstract

Recent pandemics such as Swine Flu, have caused concern for public health officials. Given the ever increasing pace at which infectious diseases can spread globally, officials must be prepared to react sooner and with greater epidemic intelligence gathering capabilities. However, state-of-the-art systems for Epidemic Intelligence have not kept the pace with the growing need for more robust public health event detection. In this paper, we propose an approach that shifts the paradigm for how public health events are detected. Instead of manually enumerating linguistic patterns to detect public health events in human language text (pattern matching); we propose the use of a statistical approaches, which instead learn the patterns of public health events in an automatic or unsupervised way.