Abstract
The new and emerging infectious diseases are an incising threat to countries due to globalisation, movement of passengers and international trade. In order to discover articles of potential importance to infectious disease emergence it is important to mine the Web with an accurate vocabulary. In this paper, we present a new methodology that combines text-mining results and visualisation approach in order to discover associations between hosts and symptoms related to emerging infectious disease outbreaks.
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Acknowledgments
This work was supported by the Ministry of Higher Education and Scientific Research of Algeria and the SONGES project (FEDER and Occitanie). We thank Renaud Lancelot (ASTRE, Cirad) and Sarah Valentin (ASTRE & TETIS, Cirad) for their expertise in epidemiological surveillance.
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Fadloun, S., Sallaberry, A., Mercier, A., Arsevska, E., Poncelet, P., Roche, M. (2018). [Demo] Integration of Text- and Web-Mining Results in EpidVis. In: Silberztein, M., Atigui, F., Kornyshova, E., Métais, E., Meziane, F. (eds) Natural Language Processing and Information Systems. NLDB 2018. Lecture Notes in Computer Science(), vol 10859. Springer, Cham. https://doi.org/10.1007/978-3-319-91947-8_45
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DOI: https://doi.org/10.1007/978-3-319-91947-8_45
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