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    Tree mining in mental health domain

    20746_downloaded_stream_202.pdf (1015.Kb)
    Access Status
    Open access
    Authors
    Hadzic, Maja
    Hadzic, Fedja
    Dillon, Tharam S.
    Date
    2008
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Hadzic, Maja and Hadzic, Fedja and Dillon, Tharam. 2008. Tree mining in mental health domain, in Sprague, R.H. (ed), Proceedings of the 41st Annual Hawaii International Conference on System Sciences, Jan 7-10, 2008, pp. 230-230. Big Island, Hawaii. IEEE Computer Society.
    Source Title
    Hawaii International Conference on System Sciences, Proceedings of the 41st Annual
    Source Conference
    Hawaii International Conference on System Sciences, 41st Annual
    DOI
    10.1109/HICSS.2008.474
    ISSN
    1530 1605
    Faculty
    Curtin Business School
    School
    Centre for Extended Enterprises and Business Intelligence
    Remarks

    Copyright 2008 IEEE

    This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.

    URI
    http://hdl.handle.net/20.500.11937/13549
    Collection
    • Curtin Research Publications
    Abstract

    The number of mentally ill people is increasing globally each year. Despite major medical advances, the identification of genetic and environmental factors responsible for mental illnesses still remains unsolved and is therefore a very active research focus today. Semi-structured data structure is predominantly used to enable the meaningful representations of the available mental health knowledge. Data mining techniques can be used to efficiently analyze these semi-structured mental health data. Tree mining algorithms can efficiently extract frequent substructures from semi-structured knowledge representation such as XML. In this paper we demonstrate effective application of the tree mining algorithms on records of mentally ill patients. The extracted data patterns can provide useful information to help in prevention of mental illness and assist in delivery of effective and efficient mental health services.

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