Auto-Extraction, Representation and Integration of a Diabetes Ontology Using Bayesian Networks

McGarry, Kenneth; Garfield, Sheila; Wermter, Stefan

doi:10.1109/cbms.2007.26

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…It not only captures and expresses the structure and semantics of the domain knowledge, but also enables developing software agents which support decision making. There has been a significant interest in developing ontology for clinical pathways in the last decade (Islam, Freytag, and Shankar, 2012;Ahmed, 2011;Lin, 2011;Nimmagadda, Nimmagadda and Dreher, 2011;Chen, and Hadzic, 2010;Chen, Bau, and Huang, 2010;McGarry, Garfield, and Wermter, 2007). Robust models have been built to represent knowledge in the ontological frame work.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Inference Engine using Ontology based Clinical Pathways for Diagnosis and Management of Diabetes

Shreelakshmi

Alse

Thantri

et al. 2014

Proceedings of the International Conference on Health Informatics

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'Clinical pathways' for Diabetes Management has attracted the attention of researchers in the last decade. Ontologies have been in use to represent knowledge pertaining to clinical pathways and to arrive at critical patient-specific decisions. This paper proposes an ontological framework to represent the diabetes related data. The main contribution of the paper is in developing an inference model that helps a General Practitioner (GP) to arrive at the most appropriate clinical pathway for a patient specific condition. The mobile application developed for this purpose makes it very useful for a medical practitioner in a remote rural location to follow a systematic process to arrive at patient specific decisions, based on the Ontological inferences received from the remote server.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Inference Engine using Ontology based Clinical Pathways for Diagnosis and Management of Diabetes

Shreelakshmi

Alse

Thantri

et al. 2014

Proceedings of the International Conference on Health Informatics

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“…Maintaining a well-curated specification is a tedious and time-consuming manual task. For improving the reaction time ( McGarry et al , 2007 ) propose inferring Bayesian networks by literature mining to generate domain-specific ontologies automatically. With the purpose of benchmarking gene associations inferred with Bayesian networks, ( Troyanskaya et al , 2003 ) propose using known GO term annotations for assessing the significance of inferred associations.…”

Section: Introductionmentioning

confidence: 99%

Bayesian assignment of gene ontology terms to gene expression experiments

Sykacek¹

2012

Bioinformatics

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Motivation: Gene expression assays allow for genome scale analyses of molecular biological mechanisms. State-of-the-art data analysis provides lists of involved genes, either by calculating significance levels of mRNA abundance or by Bayesian assessments of gene activity. A common problem of such approaches is the difficulty of interpreting the biological implication of the resulting gene lists. This lead to an increased interest in methods for inferring high-level biological information. A common approach for representing high level information is by inferring gene ontology (GO) terms which may be attributed to the expression data experiment.Results: This article proposes a probabilistic model for GO term inference. Modelling assumes that gene annotations to GO terms are available and gene involvement in an experiment is represented by a posterior probabilities over gene-specific indicator variables. Such probability measures result from many Bayesian approaches for expression data analysis. The proposed model combines these indicator probabilities in a probabilistic fashion and provides a probabilistic GO term assignment as a result. Experiments on synthetic and microarray data suggest that advantages of the proposed probabilistic GO term inference over statistical test-based approaches are in particular evident for sparsely annotated GO terms and in situations of large uncertainty about gene activity. Provided that appropriate annotations exist, the proposed approach is easily applied to inferring other high level assignments like pathways.Availability: Source code under GPL license is available from the author.Contact: peter.sykacek@boku.ac.at

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“…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%

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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Auto-Extraction, Representation and Integration of a Diabetes Ontology Using Bayesian Networks

Cited by 10 publications

References 7 publications

An Intelligent Inference Engine using Ontology based Clinical Pathways for Diagnosis and Management of Diabetes

An Intelligent Inference Engine using Ontology based Clinical Pathways for Diagnosis and Management of Diabetes

Bayesian assignment of gene ontology terms to gene expression experiments

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

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