Evaluating classification power of linked admission data sources with text mining

Simon Kocbek, Lawrence Cavedon, David Martinez, Christopher Ashley Bain, Chris Mac Manus, Gholamreza Haffari, Ingrid Zukerman, Karin Verspoor

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review


    Lung cancer is a leading cause of death in developed countries. The paper presents a text mining system using Support Vector Machines for detecting lung cancer admissions. Performance of the system using different clinical data sources is evaluated. We use radiology reports as an initial data source and add other sources, such as pathology reports and patient demographics. Results show that linking data sources significantly improves classification performance with a maximum F-Score improvement of 0.057.
    Original languageEnglish
    Title of host publicationProceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015)
    EditorsKatrina Barbuto, Louise Schaper, Karin Verspoor
    Place of PublicationAachen Germany
    PublisherRuzica Piskac
    Pages1 - 7
    Number of pages7
    Publication statusPublished - 2015
    EventBig Data in Health Analytics 2015 - Swissotel Sydney, Sydney, Australia
    Duration: 20 Oct 201521 Oct 2015
    http://ceur-ws.org/Vol-1468/ (CEUR Workshop Proceedings)


    ConferenceBig Data in Health Analytics 2015
    Abbreviated titleBigData 2015
    OtherWe are pleased to invite you to respond to our Call for Submissions for Big Data 2015, Australasia’s Big Data In Biomedicine and Healthcare Conference, to be held from 20-21 October 2015 at the Swissotel Sydney.

    With a strong focus on collaboration, this year’s theme – Big data analytics: Leveraging capability in healthcare – reflects the opportunity for healthcare practitioners, information specialists in healthcare environments, university and research institute scientists to share their knowledge.
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