Tree mining in mental health domain
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 p...
| Main Authors: | , , |
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| Format: | Conference Paper |
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IEEE Xplore
2008
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| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/13549 |
| _version_ | 1848748375995842560 |
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| author | Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. |
| author_facet | Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. |
| author_sort | Hadzic, Maja |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T07:04:03Z |
| format | Conference Paper |
| id | curtin-20.500.11937-13549 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T07:04:03Z |
| publishDate | 2008 |
| publisher | IEEE Xplore |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-135492017-09-13T15:00:25Z Tree mining in mental health domain Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. data mining Australia bioinformatics diseases distributed databases 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. 2008 Conference Paper http://hdl.handle.net/20.500.11937/13549 10.1109/HICSS.2008.474 IEEE Xplore fulltext |
| spellingShingle | data mining Australia bioinformatics diseases distributed databases Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. Tree mining in mental health domain |
| title | Tree mining in mental health domain |
| title_full | Tree mining in mental health domain |
| title_fullStr | Tree mining in mental health domain |
| title_full_unstemmed | Tree mining in mental health domain |
| title_short | Tree mining in mental health domain |
| title_sort | tree mining in mental health domain |
| topic | data mining Australia bioinformatics diseases distributed databases |
| url | http://hdl.handle.net/20.500.11937/13549 |