Ontology based data warehouse modeling and managing ecology of human body for disease and drug prescription management

Nimmagadda, Shastri L.; Nimmagadda, Sashi K; Dreher, Heinz

doi:10.1109/dest.2008.4635209

Cited by 15 publications

(16 citation statements)

References 20 publications

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“…The findings of the current study are consistent with our previous review (Liaw et al 2013) that the ontological approach to develop DQ is poorly evaluated. However, most agreed that DQ is a multidimensional construct (Devillers et al 2007;Nimmagadda et al 2008); with completeness, accuracy, correctness, consistency and timeliness being the most commonly used dimensions. A few studies examined ontology-based approaches to support data consistency and accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…However, they do not compare ontologies with other data models. Studies to demonstrate the impact of ontology-based implementations included clinical decision support systems (Brüggemann and Grüning 2009;Min et al 2009;Topalis et al 2011) for information management (O'Donoghue et al 2009;Young et al 2009), diagnosis (Nimmagadda et al 2008), clinical data analysis and management . A few studies examined ontology-based approaches to support data consistency (Esposito 2008a) and accuracy.…”

Section: %mentioning

confidence: 99%

“…The application of ontological approaches to data quality management addressed the following issues: data quality problems and errors (Brüggemann and Grüning 2009), data heterogeneity problem (Min et al 2009), semantic decision making (Lee et al 2009), efficient services , procedures concerning the acquisition of data (Nimmagadda et al 2008), classification and identification of specific patients types (Lee et al 2009;Wang et al 2007), data collection, data sharing and data integration (Min et al 2009;O'Donoghue et al 2009;Perez-Rey et al 2006;Young et al 2009). There were no studies that examined efficiency or effectiveness of ontology-based models in DQ management.…”

Section: %mentioning

confidence: 99%

“…Most studies have used precision and sensitivity (recall) to assess the accuracy of ontology-based approaches in health domains (Brewster et al 2004;Euzenat 2007;Gangemi et al 2006;McGarry et al 2007;Min et al 2009;Pathak et al 2012a, (Nimmagadda et al 2008) To provide a solution to problems around handling increasing amounts of clinical information and solves some issues related to managing large -Simulate human body disorders into metadata through ontology based data warehouse modelling…”

Section: %mentioning

confidence: 99%

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Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

et al. 2014

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This systematic review examined the current state of conceptualization and specification of data quality and the role of ontology based approaches to develop data quality based on "fitness for purpose" within the health context. A literature review was conducted of all English language studies, from January 2000-March 2013, which addressed data/information quality, fitness for purpose of data, used and implemented ontology-based approaches. Included papers were critically appraised with a "context-mechanism-impacts/outcomes" overlay. We screened 315 papers, excluded 36 duplicates, 182 on abstract review and 46 on full-text review; leaving 52 papers for critical appraisal. Six papers conceptualized data quality within the "fitness for purpose" definition. While most agree with a multidimensional definition of DQ, there is little consensus on a conceptual framework. We found no reports of systematic and comprehensive ontological approaches to DQ based on fitness for purpose or use. However, 16 papers used ontology-specified implementations in DQ improvement, with most of them focusing on some dimensions of DQ such as completeness, accuracy, correctness, consistency and timeliness. The majority of papers described the processes of the development of DQ in various information systems. There were few evaluative studies, including any comparing ontological with non-ontological approaches, on the assessment of clinical data quality and the performance of the application.

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Section: Discussionmentioning

confidence: 99%

Section: %mentioning

confidence: 99%

Section: %mentioning

confidence: 99%

Section: %mentioning

confidence: 99%

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Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

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“…Management of ecology and issues surrounding human ecosystems have been discussed in [4]. Several domain ontologies relevant to medical and other engineering applications have been investigated by [5] and [8], demonstrating several multidimensional data structuring and data integration issues.…”

Section: Previous Literaturementioning

confidence: 99%

Multidimensional ontology modeling of human digital ecosystems affected by social behavioural data patterns

Nimmagadda

Dreher

2010

4th IEEE International Conference on Digital Ecosystems and Technologies

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Relational and hierarchical data modeling studies are carried out, using simple and explicit comparison based ontology. The comparison is basically performed on relationally and hierarchically structured data entities/dimensions. This methodology is adopted to understand the human ecosystem that is affected by human behavioural and social disorder data patterns. For example, the comparison may be made among human systems, which could be between male and female, fat and slim, disabled and normal (physical impairment), again normal and abnormal (psychological), smokers and non-smokers and among different age group domains. There could be different hierarchies among which, different super-type dimensions are conceptualized into several subtype dimensions and integrated them by connecting the interrelated several common data attributes. Domain ontologies are built based on the known-knowledge mining and thus unknown relationships are modeled that are affected by social behaviour data patterns. This study is useful in understanding human situations, behavioral patterns and social ecology that can facilitate health and medical practitioners, social workers and psychologists, while treating their patients and clients.Index Terms-Ontology, digital ecosystems, social behavioural patterns, health and medicine.4th IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2010)

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Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm

Nimmagadda

Rudra

2016

Big Data and Learning Analytics in Higher Education

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Ontology based data warehouse modeling and managing ecology of human body for disease and drug prescription management

Cited by 15 publications

References 20 publications

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

Multidimensional ontology modeling of human digital ecosystems affected by social behavioural data patterns

Managing the Embedded Digital Ecosystems (EDE) Using Big Data Paradigm

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