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
    EventInternational Symposium on Information Management and Big Data 2014 - Cusco, Peru
    Duration: 8 Sep 201410 Sep 2014
    Conference number: 1st
    https://simbig.org/SIMBig2014/

    Publication series

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

    Conference

    ConferenceInternational Symposium on Information Management and Big Data 2014
    Abbreviated titleSIMBig 2014
    CountryPeru
    CityCusco
    Period8/09/1410/09/14
    Internet address

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