Tree mining in mental health domain
|dc.contributor.author||Dillon, Tharam S.|
|dc.identifier.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.|
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.
|dc.title||Tree mining in mental health domain|
|dcterms.source.title||Hawaii International Conference on System Sciences, Proceedings of the 41st Annual|
|dcterms.source.series||Hawaii International Conference on System Sciences, Proceedings of the 41st Annual|
|dcterms.source.conference||Hawaii International Conference on System Sciences, 41st Annual|
|dcterms.source.conference-start-date||07-10 Jan 2008|
Copyright 2008 IEEE
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|curtin.department||Centre for Extended Enterprises and Business Intelligence|
|curtin.faculty||Curtin Business School|