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    Domain driven tree mining of semi-structured mental health information

    Access Status
    Fulltext not available
    Authors
    Hadzic, Fedja
    Dillon, Tharam S.
    Hadzic, Maja
    Date
    2009
    Type
    Book Chapter
    
    Metadata
    Show full item record
    Citation
    Hadzic, Maja, Hadzic, Fedja and Dillon, Tharam S. 2009. Domain driven tree mining of semi-structured mental health information, in Longbing Cao, Philip S. Yu, Chengqi Zhang and Huaifeng Zhang (ed), Data mining for business applications. pp. 127-141. Heidelberg: Springer.
    Source Title
    Data mining for business applications
    Additional URLs
    http://www.springerlink.com/content/978-0-387-79419-8
    ISBN
    9780387794198
    Faculty
    Curtin Business School
    The Digital Ecosystems and Business Intelligence Institute (DEBII)
    School
    Digital Ecosystems and Business Intelligence Institute (DEBII)
    URI
    http://hdl.handle.net/20.500.11937/28749
    Collection
    • Curtin Research Publications
    Abstract

    The World Health Organization predicted that depression would be the world's leading cause of disability by 2020. This is calling for urgent interventions. As most mental illnesses are caused by a number of genetic and environmental factors and many different types of mental illness exist, the identification of a precise combination of genetic and environmental causes for each mental illness type is crucial in the prevention and effective treatment of mental illness. Sophisticated data analysis tools, such as data mining, can greatly contribute in the identification of precise patterns of genetic and environmental factors and greatly help the prevention and intervention strategies. One of the factors that complicates data mining in this area is that much of the information is not in strictly structured form. In this paper, we demonstrate the application of tree mining algorithms on semi-structured mental health information. The extracted data patterns can provide useful information to help in the prevention of mental illness, and assist in the delivery of effective and efficient mental health services.

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