Domain driven tree mining of semi-structured mental health information
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...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Book Chapter |
| Published: |
Springer
2009
|
| Online Access: | http://www.springerlink.com/content/978-0-387-79419-8 http://hdl.handle.net/20.500.11937/28749 |
| _version_ | 1848752620288606208 |
|---|---|
| author | Hadzic, Fedja Dillon, Tharam S. Hadzic, Maja |
| author2 | Longbing Cao |
| author_facet | Longbing Cao Hadzic, Fedja Dillon, Tharam S. Hadzic, Maja |
| author_sort | Hadzic, Fedja |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T08:11:31Z |
| format | Book Chapter |
| id | curtin-20.500.11937-28749 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:11:31Z |
| publishDate | 2009 |
| publisher | Springer |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-287492022-12-09T06:09:41Z Domain driven tree mining of semi-structured mental health information Hadzic, Fedja Dillon, Tharam S. Hadzic, Maja Longbing Cao Philip S. Yu Chengqi Zhang Huaifeng Zhang 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. 2009 Book Chapter http://hdl.handle.net/20.500.11937/28749 http://www.springerlink.com/content/978-0-387-79419-8 Springer restricted |
| spellingShingle | Hadzic, Fedja Dillon, Tharam S. Hadzic, Maja Domain driven tree mining of semi-structured mental health information |
| title | Domain driven tree mining of semi-structured mental health information |
| title_full | Domain driven tree mining of semi-structured mental health information |
| title_fullStr | Domain driven tree mining of semi-structured mental health information |
| title_full_unstemmed | Domain driven tree mining of semi-structured mental health information |
| title_short | Domain driven tree mining of semi-structured mental health information |
| title_sort | domain driven tree mining of semi-structured mental health information |
| url | http://www.springerlink.com/content/978-0-387-79419-8 http://hdl.handle.net/20.500.11937/28749 |