Mining of health information from ontologies
Data mining techniques can be used to efficiently analyze semi-structured data. Semi-structured data are predominantly used within the health domain as they enable meaningful representations of the health information. Tree mining algorithms can efficiently extract frequent substructures from semi-st...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference Paper |
| Published: |
Instinct Press
2008
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/5642 |
| _version_ | 1848744853770338304 |
|---|---|
| author | Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. |
| author2 | Azevedo, L. |
| author_facet | Azevedo, L. Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. |
| author_sort | Hadzic, Maja |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Data mining techniques can be used to efficiently analyze semi-structured data. Semi-structured data are predominantly used within the health domain as they enable meaningful representations of the health information. Tree mining algorithms can efficiently extract frequent substructures from semi-structured knowledge representations. In this paper, we demonstrate application of the tree mining algorithms on the health information. We illustrate this on an example of Human Disease Ontology (HDO) which represents information about diseases in 4 ?dimensions?: (1) disease types, (2) phenotype (observable characteristics of an organism) or symptoms (3) causes related to the disease, namely genetic causes, environmental causes or micro-organisms, and (4) treatments available for the disease. The extracted data patterns can provide useful information to help in disease prevention, and assist in delivery of effective and efficient health services |
| first_indexed | 2025-11-14T06:08:04Z |
| format | Conference Paper |
| id | curtin-20.500.11937-5642 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:08:04Z |
| publishDate | 2008 |
| publisher | Instinct Press |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-56422017-01-30T10:47:36Z Mining of health information from ontologies Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. Azevedo, L. Landral, A. Ontology mining Health information system Data mining Tree mining Ontology Human disease ontology Human disease study Data mining techniques can be used to efficiently analyze semi-structured data. Semi-structured data are predominantly used within the health domain as they enable meaningful representations of the health information. Tree mining algorithms can efficiently extract frequent substructures from semi-structured knowledge representations. In this paper, we demonstrate application of the tree mining algorithms on the health information. We illustrate this on an example of Human Disease Ontology (HDO) which represents information about diseases in 4 ?dimensions?: (1) disease types, (2) phenotype (observable characteristics of an organism) or symptoms (3) causes related to the disease, namely genetic causes, environmental causes or micro-organisms, and (4) treatments available for the disease. The extracted data patterns can provide useful information to help in disease prevention, and assist in delivery of effective and efficient health services 2008 Conference Paper http://hdl.handle.net/20.500.11937/5642 Instinct Press fulltext |
| spellingShingle | Ontology mining Health information system Data mining Tree mining Ontology Human disease ontology Human disease study Hadzic, Maja Hadzic, Fedja Dillon, Tharam S. Mining of health information from ontologies |
| title | Mining of health information from ontologies |
| title_full | Mining of health information from ontologies |
| title_fullStr | Mining of health information from ontologies |
| title_full_unstemmed | Mining of health information from ontologies |
| title_short | Mining of health information from ontologies |
| title_sort | mining of health information from ontologies |
| topic | Ontology mining Health information system Data mining Tree mining Ontology Human disease ontology Human disease study |
| url | http://hdl.handle.net/20.500.11937/5642 |