Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare
This paper describes advances in automated health service selection and composition in the Ambient Assisted Living (AAL) domain. We apply a Service Value Network (SVN) approach to automatically match medical practice recommendations to health services based on sensor readings in a home care context....
| Main Authors: | , , |
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| Other Authors: | |
| Format: | Conference Paper |
| Published: |
ACIS
2012
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| Online Access: | http://dro.deakin.edu.au/view/DU:30049137 http://hdl.handle.net/20.500.11937/45683 |
| _version_ | 1848757353273360384 |
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| author | Meersman, Davor De Leenheer, P. Hadzic, Fedja |
| author2 | John Lamp |
| author_facet | John Lamp Meersman, Davor De Leenheer, P. Hadzic, Fedja |
| author_sort | Meersman, Davor |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | This paper describes advances in automated health service selection and composition in the Ambient Assisted Living (AAL) domain. We apply a Service Value Network (SVN) approach to automatically match medical practice recommendations to health services based on sensor readings in a home care context. Medical practice recommendations are extracted from National Health and Medical Research Council (NHMRC) guidelines. Service networks are derived from Medicare Benefits Schedule (MBS) listings. Service provider rules are further formalised using Semantics of Business Vocabulary and Business Rules (SBVR), which allows business participants to identify and define machine-readable rules. We demonstrate our work by applying an SVN composition process to patient profiles in the context of Type 2 Diabetes Management. |
| first_indexed | 2025-11-14T09:26:45Z |
| format | Conference Paper |
| id | curtin-20.500.11937-45683 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T09:26:45Z |
| publishDate | 2012 |
| publisher | ACIS |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-456832017-01-30T15:22:36Z Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare Meersman, Davor De Leenheer, P. Hadzic, Fedja John Lamp This paper describes advances in automated health service selection and composition in the Ambient Assisted Living (AAL) domain. We apply a Service Value Network (SVN) approach to automatically match medical practice recommendations to health services based on sensor readings in a home care context. Medical practice recommendations are extracted from National Health and Medical Research Council (NHMRC) guidelines. Service networks are derived from Medicare Benefits Schedule (MBS) listings. Service provider rules are further formalised using Semantics of Business Vocabulary and Business Rules (SBVR), which allows business participants to identify and define machine-readable rules. We demonstrate our work by applying an SVN composition process to patient profiles in the context of Type 2 Diabetes Management. 2012 Conference Paper http://hdl.handle.net/20.500.11937/45683 http://dro.deakin.edu.au/view/DU:30049137 ACIS fulltext |
| spellingShingle | Meersman, Davor De Leenheer, P. Hadzic, Fedja Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare |
| title | Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare |
| title_full | Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare |
| title_fullStr | Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare |
| title_full_unstemmed | Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare |
| title_short | Patient and Business Rules Extraction and Formalisation Using SVN and SBVR for Automated Healthcare |
| title_sort | patient and business rules extraction and formalisation using svn and sbvr for automated healthcare |
| url | http://dro.deakin.edu.au/view/DU:30049137 http://hdl.handle.net/20.500.11937/45683 |