On robust methodologies for managing public health care systems
Authors focus on ontology-based multidimensional data warehousing and mining methodologies, addressing various issues on organizing, reporting and documenting diabetic cases and their associated ailments, including causalities. Map and other diagnostic data views, depicting similarity and comparison...
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| Format: | Journal Article |
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Molecular Diversity Preservation International (MDPI)
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/3221 |
| _version_ | 1848744172205375488 |
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| author | Nimmagadda, Shastri Dreher, Heinz |
| author_facet | Nimmagadda, Shastri Dreher, Heinz |
| author_sort | Nimmagadda, Shastri |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Authors focus on ontology-based multidimensional data warehousing and mining methodologies, addressing various issues on organizing, reporting and documenting diabetic cases and their associated ailments, including causalities. Map and other diagnostic data views, depicting similarity and comparison of attributes, extracted from warehouses, are used for understanding the ailments, based on gender, age, geography, food-habits and other hereditary event attributes. In addition to rigor on data mining and visualization, an added focus is on values of interpretation of data views, from processed full-bodied diagnosis, subsequent prescription and appropriate medications. The proposed methodology, is a robust back-end application, for web-based patient-doctor consultations and e-Health care management systems through which, billions of dollars spent on medical services, can be saved, in addition to improving quality of life and average life span of a person. Government health departments and agencies, private and government medical practitioners including social welfare organizations are typical users of these systems. |
| first_indexed | 2025-11-14T05:57:14Z |
| format | Journal Article |
| id | curtin-20.500.11937-3221 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T05:57:14Z |
| publishDate | 2014 |
| publisher | Molecular Diversity Preservation International (MDPI) |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-32212021-01-28T04:19:03Z On robust methodologies for managing public health care systems Nimmagadda, Shastri Dreher, Heinz visualization data mining food ontologies data warehousing diabetes data - interpretation Authors focus on ontology-based multidimensional data warehousing and mining methodologies, addressing various issues on organizing, reporting and documenting diabetic cases and their associated ailments, including causalities. Map and other diagnostic data views, depicting similarity and comparison of attributes, extracted from warehouses, are used for understanding the ailments, based on gender, age, geography, food-habits and other hereditary event attributes. In addition to rigor on data mining and visualization, an added focus is on values of interpretation of data views, from processed full-bodied diagnosis, subsequent prescription and appropriate medications. The proposed methodology, is a robust back-end application, for web-based patient-doctor consultations and e-Health care management systems through which, billions of dollars spent on medical services, can be saved, in addition to improving quality of life and average life span of a person. Government health departments and agencies, private and government medical practitioners including social welfare organizations are typical users of these systems. 2014 Journal Article http://hdl.handle.net/20.500.11937/3221 10.3390/ijerph110101106 http://creativecommons.org/licenses/by/3.0 Molecular Diversity Preservation International (MDPI) fulltext |
| spellingShingle | visualization data mining food ontologies data warehousing diabetes data - interpretation Nimmagadda, Shastri Dreher, Heinz On robust methodologies for managing public health care systems |
| title | On robust methodologies for managing public health care systems |
| title_full | On robust methodologies for managing public health care systems |
| title_fullStr | On robust methodologies for managing public health care systems |
| title_full_unstemmed | On robust methodologies for managing public health care systems |
| title_short | On robust methodologies for managing public health care systems |
| title_sort | on robust methodologies for managing public health care systems |
| topic | visualization data mining food ontologies data warehousing diabetes data - interpretation |
| url | http://hdl.handle.net/20.500.11937/3221 |