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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

Map views showing diabetes deaths for different regions (male and female, in thousands).
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getmorefigures.php?uid=PMC3924495&req=5

ijerph-11-01106-f017: Map views showing diabetes deaths for different regions (male and female, in thousands).

Mentions: In the first stage, basic demographic and periodic information about the patients are presented to the doctor analysts. The goal here is to allow the doctors to know the basic information and understanding of the patient, how different attributes, such as age, race, etc. impact the disease and also the circumstances that population is living around the patient. The information is presented in several data mining views, for example, histogram graphs, and plot and 2D/3D maps views, for easy interpretation. Two types of information can be displayed: the absolute proportion or the relative proportion. Data instances collected worldwide, suggest as shown in Figure 14, Figure 15, Figure 16, Figure 17, Figure 18, Figure 19, Figure 20 and Figure 21, the proportion of the different race-age populations is displayed, having eye disease. The race-age group with the highest disease proportion is assigned to be 100%, while the rest of the group proportions are adjusted accordingly. Each bar represents an age group while different colors within a bar denote different races. This provides a common basis for comparison. In one glance, the doctor is able to tell which factor dominates in terms of the number of patients, having a particular eye disease and how much are the deviations among the groups over the years.


On robust methodologies for managing public health care systems.

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

Map views showing diabetes deaths for different regions (male and female, in thousands).
© Copyright Policy
Related In: Results  -  Collection

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

ijerph-11-01106-f017: Map views showing diabetes deaths for different regions (male and female, in thousands).
Mentions: In the first stage, basic demographic and periodic information about the patients are presented to the doctor analysts. The goal here is to allow the doctors to know the basic information and understanding of the patient, how different attributes, such as age, race, etc. impact the disease and also the circumstances that population is living around the patient. The information is presented in several data mining views, for example, histogram graphs, and plot and 2D/3D maps views, for easy interpretation. Two types of information can be displayed: the absolute proportion or the relative proportion. Data instances collected worldwide, suggest as shown in Figure 14, Figure 15, Figure 16, Figure 17, Figure 18, Figure 19, Figure 20 and Figure 21, the proportion of the different race-age populations is displayed, having eye disease. The race-age group with the highest disease proportion is assigned to be 100%, while the rest of the group proportions are adjusted accordingly. Each bar represents an age group while different colors within a bar denote different races. This provides a common basis for comparison. In one glance, the doctor is able to tell which factor dominates in terms of the number of patients, having a particular eye disease and how much are the deviations among the groups over the years.

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