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    A transparent and transportable methodology for evaluating Data Linkage software

    171811_Ferrante_Boyd_MANUSCRIPT.pdf (650.4Kb)
    Access Status
    Open access
    Authors
    Ferrante, Anna
    Boyd, James
    Date
    2011
    Type
    Journal Article
    
    Metadata
    Show full item record
    Citation
    Ferrante, Anna and Boyd, James. 2012. A transparent and transportable methodology for evaluating Data Linkage software. Journal of Biomedical Informatics. 45 (1): pp. 165-172.
    Source Title
    Journal of Biomedical Informatics
    DOI
    10.1016/j.jbi.2011.10.006
    ISSN
    1532-0464
    School
    Centre for Population Health
    Remarks

    NOTICE: this is the author’s version of a work that was accepted for publication in Journal of Biomedical Informatics. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal of Biomedical Informatics, 45 (1) 2012 http://dx.doi.org/10.1016/j.jbi.2011.10.006

    URI
    http://hdl.handle.net/20.500.11937/4208
    Collection
    • Curtin Research Publications
    Abstract

    There has been substantial growth in DataLinkage (DL) activities in recent years. This reflects growth in both the demand for, and the supply of, linked or linkable data. Increased utilisation of DL “services” has brought with it increased need for impartial information about the suitability and performance capabilities of DL software programs and packages. Although evaluations of DL software exist; most have been restricted to the comparison of two or three packages. Evaluations of a large number of packages are rare because of the time and resource burden placed on the evaluators and the need for a suitable “gold standard” evaluation dataset. In this paper we present an evaluation methodology that overcomes a number of these difficulties. Our approach involves the generation and use of representative synthetic data; the execution of a series of linkages using a pre-defined linkage strategy; and the use of standard linkage quality metrics to assess performance. The methodology is both transparent and transportable, producing genuinely comparable results. The methodology was used by the Centre for DataLinkage (CDL) at Curtin University in an evaluation of ten DL software packages. It is also being used to evaluate larger linkage systems (not just packages). The methodology provides a unique opportunity to benchmark the quality of linkages in different operational environments.

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