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

    Abstract

    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
    Volume1468
    Publication statusPublished - 2015
    EventAnnual Conference in Big Data in Health analytics - Swissotel Sydney, Sydney, Australia
    Duration: 20 Oct 201521 Oct 2015
    http://ceur-ws.org/Vol-1468/ (CEUR Workshop Proceedings)
    https://www.hisa.org.au/bigdata/science2015/

    Conference

    ConferenceAnnual Conference in Big Data in Health analytics
    Abbreviated titleBigData 2015
    CountryAustralia
    CitySydney
    Period20/10/1521/10/15
    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.
    Internet address

    Cite this

    Kocbek, S., Cavedon, L., Martinez, D., Bain, C. A., Mac Manus, C., Haffari, G., ... Verspoor, K. (2015). Evaluating classification power of linked admission data sources with text mining. In K. Barbuto, L. Schaper, & K. Verspoor (Eds.), Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015) (Vol. 1468, pp. 1 - 7). Aachen Germany: Ruzica Piskac.
    Kocbek, Simon ; Cavedon, Lawrence ; Martinez, David ; Bain, Christopher Ashley ; Mac Manus, Chris ; Haffari, Gholamreza ; Zukerman, Ingrid ; Verspoor, Karin. / Evaluating classification power of linked admission data sources with text mining. Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015). editor / Katrina Barbuto ; Louise Schaper ; Karin Verspoor. Vol. 1468 Aachen Germany : Ruzica Piskac, 2015. pp. 1 - 7
    @inproceedings{4aab1d72702549adb0ceb8b23175e5fd,
    title = "Evaluating classification power of linked admission data sources with text mining",
    abstract = "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.",
    author = "Simon Kocbek and Lawrence Cavedon and David Martinez and Bain, {Christopher Ashley} and {Mac Manus}, Chris and Gholamreza Haffari and Ingrid Zukerman and Karin Verspoor",
    year = "2015",
    language = "English",
    volume = "1468",
    pages = "1 -- 7",
    editor = "Katrina Barbuto and Louise Schaper and Karin Verspoor",
    booktitle = "Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015)",
    publisher = "Ruzica Piskac",

    }

    Kocbek, S, Cavedon, L, Martinez, D, Bain, CA, Mac Manus, C, Haffari, G, Zukerman, I & Verspoor, K 2015, Evaluating classification power of linked admission data sources with text mining. in K Barbuto, L Schaper & K Verspoor (eds), Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015). vol. 1468, Ruzica Piskac, Aachen Germany, pp. 1 - 7, Annual Conference in Big Data in Health analytics, Sydney, Australia, 20/10/15.

    Evaluating classification power of linked admission data sources with text mining. / Kocbek, Simon; Cavedon, Lawrence; Martinez, David; Bain, Christopher Ashley; Mac Manus, Chris; Haffari, Gholamreza; Zukerman, Ingrid; Verspoor, Karin.

    Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015). ed. / Katrina Barbuto; Louise Schaper; Karin Verspoor. Vol. 1468 Aachen Germany : Ruzica Piskac, 2015. p. 1 - 7.

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

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    T1 - Evaluating classification power of linked admission data sources with text mining

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    AU - Cavedon, Lawrence

    AU - Martinez, David

    AU - Bain, Christopher Ashley

    AU - Mac Manus, Chris

    AU - Haffari, Gholamreza

    AU - Zukerman, Ingrid

    AU - Verspoor, Karin

    PY - 2015

    Y1 - 2015

    N2 - 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.

    AB - 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.

    UR - http://ceur-ws.org/Vol-1468/bd2015_kocbek.pdf

    M3 - Conference Paper

    VL - 1468

    SP - 1

    EP - 7

    BT - Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015)

    A2 - Barbuto, Katrina

    A2 - Schaper, Louise

    A2 - Verspoor, Karin

    PB - Ruzica Piskac

    CY - Aachen Germany

    ER -

    Kocbek S, Cavedon L, Martinez D, Bain CA, Mac Manus C, Haffari G et al. Evaluating classification power of linked admission data sources with text mining. In Barbuto K, Schaper L, Verspoor K, editors, Proceedings of the Scientific Stream at Big Data in Health Analytics 2015 (BigData 2015). Vol. 1468. Aachen Germany: Ruzica Piskac. 2015. p. 1 - 7