Text mining for lung cancer cases over large patient admission data

David Martinez, Lawrence Cavedon, Zaf Alam, Christopher Bain, Karin Verspoor

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

    2 Citations (Scopus)

    Abstract

    We describe a text mining system running over a large clinical repository for the detection of lung cancer admissions, and evaluate its performance over different scenarios. Our results show that a Machine Learning classifier is able to obtain significant gains over a keyword-matching approach, and also that combining patient metadata with the textual content further improves performance.

    Original languageEnglish
    Title of host publication1st Symposium on Information Management and Big Data - Proceedings
    EditorsJ. A. Lossio-Ventura, H. Alatrista-Salas
    Place of PublicationPeru
    PublisherSIMBig
    Pages24-25
    Number of pages2
    Volume1149
    Publication statusPublished - 2014
    EventBig Data 2014 - Pullman Melbourne Albert Park, Melbourne, Australia
    Duration: 3 Apr 20144 Apr 2014
    https://hisa.site-ym.com/mpage/bigdata2014

    Publication series

    NameCEUR Workshop Proceedings
    PublisherRheinisch-Westfaelische Technische Hochschule Aachen * Lehrstuhl Informatik V
    ISSN (Print)1613-0073

    Conference

    ConferenceBig Data 2014
    CountryAustralia
    CityMelbourne
    Period3/04/144/04/14
    Internet address

    Cite this

    Martinez, D., Cavedon, L., Alam, Z., Bain, C., & Verspoor, K. (2014). Text mining for lung cancer cases over large patient admission data. In J. A. Lossio-Ventura, & H. Alatrista-Salas (Eds.), 1st Symposium on Information Management and Big Data - Proceedings (Vol. 1149, pp. 24-25). (CEUR Workshop Proceedings). SIMBig.