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On robust methodologies for managing public health care systems.

Nimmagadda SL, Dreher HV - Int J Environ Res Public Health (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Information Systems, CBS, Curtin University, Perth, 6102 WA, Australia. shastri.nimmagadda2011@gmail.com.

ABSTRACT
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.

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Related in: MedlinePlus

Multidimensional modeling of data instances relevant to antioxidants and their associated foods affecting the blood sugars.
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ijerph-11-01106-f005: Multidimensional modeling of data instances relevant to antioxidants and their associated foods affecting the blood sugars.

Mentions: In our frameworks, ontology is a description of concepts/contexts and relationships among various either entities and or dimensions that exist in the knowledge domains of food-diabetes. Ontology deals with queries on what entities and or dimensions exist in a given domain and how such entities or dimensions can be grouped, related within hierarchical, relational and networked data structures. Ontology Web Language (OWL) is a knowledge representation, and in our case, knowledge is expressed in the form of various fine-grained data schemas as represented in Figure 3, Figure 4 and Figure 5, which explore connections from multiple domains, which can make data mining effective, including visualization and interpretation. Multidimensional data relationship diagrams drawn, are modelled in Oracle-driven, Windows-based high speed computing workstations. Availability of heterogeneity and multidimensionality nature of data sources on food-diabetes has, in fact, motivated us the necessicity of developing an ontology-based warehouse for accommodating multidimensional healthcare metadata structures.


On robust methodologies for managing public health care systems.

Nimmagadda SL, Dreher HV - Int J Environ Res Public Health (2014)

Multidimensional modeling of data instances relevant to antioxidants and their associated foods affecting the blood sugars.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3924495&req=5

ijerph-11-01106-f005: Multidimensional modeling of data instances relevant to antioxidants and their associated foods affecting the blood sugars.
Mentions: In our frameworks, ontology is a description of concepts/contexts and relationships among various either entities and or dimensions that exist in the knowledge domains of food-diabetes. Ontology deals with queries on what entities and or dimensions exist in a given domain and how such entities or dimensions can be grouped, related within hierarchical, relational and networked data structures. Ontology Web Language (OWL) is a knowledge representation, and in our case, knowledge is expressed in the form of various fine-grained data schemas as represented in Figure 3, Figure 4 and Figure 5, which explore connections from multiple domains, which can make data mining effective, including visualization and interpretation. Multidimensional data relationship diagrams drawn, are modelled in Oracle-driven, Windows-based high speed computing workstations. Availability of heterogeneity and multidimensionality nature of data sources on food-diabetes has, in fact, motivated us the necessicity of developing an ontology-based warehouse for accommodating multidimensional healthcare metadata structures.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: School of Information Systems, CBS, Curtin University, Perth, 6102 WA, Australia. shastri.nimmagadda2011@gmail.com.

ABSTRACT
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.

Show MeSH
Related in: MedlinePlus