Data linkage in medical science using the resource description framework: the AVERT model - O'Sullivan, Declan; Little, Mark; Hederman, Lucy (My University, 2018)
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.
PUBLISHED
Keywords: evidence-based medicine; information and knowledge management; data security and confidentiality; resource description framework; semantic web; linked data; electronic health records; Digital Engagement; Knowledge and data engineering