Data linkage in medical science using the resource description framework: the AVERT model
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Journal ArticleDate:
2018Access:
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Brian Reddy, Brett Houlding, Lucy Hederman, Mark Canney, Christophe Debruyne, Ciaran O'Brien, Alan Meehan, Declan O'Sullivan, Mark A Little,, Data linkage in medical science using the resource description framework: the AVERT model, HRB Open Research, 1, 20, 2018, 1-15Download Item:
Abstract:
There is an ongoing challenge as to how best manage and understand ‘big data’ in precision medicine settings. This paper describes the potential for a Linked Data approach, using a Resource Description Framework (RDF) model, to combine multiple datasets with temporal and spatial elements of varying dimensionality. This “AVERT model” provides a framework for converting multiple standalone files of various formats, from both clinical and environmental settings, into a single data source. This data source can thereafter be queried effectively, shared with outside parties, more easily understood by multiple stakeholders using standardized vocabularies, incorporating provenance metadata and supporting temporo-spatial reasoning. The approach has further advantages in terms of data sharing, security and subsequent analysis. We use a case study relating to anti-Glomerular Basement Membrane (GBM) disease, a rare autoimmune condition, to illustrate a technical proof of concept for the AVERT model.
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http://people.tcd.ie/osulldpshttp://people.tcd.ie/hederman
http://people.tcd.ie/mlittle
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HRB Open Research1
20
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evidence-based medicine, information and knowledge management, data security and confidentiality, resource description framework, semantic web, linked data, electronic health recordsSubject (TCD):
Digital Engagement , Knowledge and data engineeringDOI:
http://dx.doi.org/10.12688/hrbopenres.12851.1Metadata
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