BioCaster: Detecting public health rumors with a Web-based text mining system

Nigel Collier, Son Doan, Ai Kawazoe, Reiko Matsuda Goodwin, Mike Conway, Yoshio Tateno, Quoc Hung Ngo, Dinh Dien, Asanee Kawtrakul, Koichi Takeuchi, Mika Shigematsu, Kiyosu Taniguchi

Research output: Contribution to journalArticle

147 Citations (Scopus)

Abstract

Summary: BioCaster is an ontology-based text mining system for detecting and tracking the distribution of infectious disease outbreaks from linguistic signals on the Web. The system continuously analyzes documents reported from over 1700 RSS feeds, classifies them for topical relevance and plots them onto a Google map using geocoded information. The background knowledge for bridging the gap between Layman's terms and formal-coding systems is contained in the freely available BioCaster ontology which includes information in eight languages focused on the epidemiological role of pathogens as well as geographical locations with their latitudes/longitudes. The system consists of four main stages: topic classification, named entity recognition (NER), disease/location detection and event recognition. Higher order event analysis is used to detect more precisely specified warning signals that can then be notified to registered users via email alerts. Evaluation of the system for topic recognition and entity identification is conducted on a gold standard corpus of annotated news articles.

Original languageEnglish
Pages (from-to)2940-2941
Number of pages2
JournalBioinformatics
Volume24
Issue number24
DOIs
Publication statusPublished - Dec 1 2008

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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  • Cite this

    Collier, N., Doan, S., Kawazoe, A., Goodwin, R. M., Conway, M., Tateno, Y., Ngo, Q. H., Dien, D., Kawtrakul, A., Takeuchi, K., Shigematsu, M., & Taniguchi, K. (2008). BioCaster: Detecting public health rumors with a Web-based text mining system. Bioinformatics, 24(24), 2940-2941. https://doi.org/10.1093/bioinformatics/btn534