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Big data and data repurposing - using existing data to answer new questions in vascular dementia research

 
: Doubal, F.N.; Ali, M.; Batty, G.D.; Charidimou, A.; Eriksdotter, M.; Hofmann-Apitius, M.; Kim, Y.-H.; Levine, D.A.; Mead, G.; Mucke, H.A.M.; Ritchie, C.W.; Roberts, C.J.; Russ, T.C.; Stewart, R.; Whiteley, W.; Quinn, T.J.

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Volltext (PDF; )

BMC neurology. Online journal 17 (2017), Art. 72, 10 S.
http://www.biomedcentral.com/1471-2377/
ISSN: 1471-2377
Englisch
Zeitschriftenaufsatz, Elektronische Publikation
Fraunhofer SCAI ()

Abstract
Introduction
Traditional approaches to clinical research have, as yet, failed to provide effective treatments for vascular dementia (VaD). Novel approaches to collation and synthesis of data may allow for time and cost efficient hypothesis generating and testing. These approaches may have particular utility in helping us understand and treat a complex condition such as VaD.
Methods
We present an overview of new uses for existing data to progress VaD research. The overview is the result of consultation with various stakeholders, focused literature review and learning from the group’s experience of successful approaches to data repurposing. In particular, we benefitted from the expert discussion and input of delegates at the 9th International Congress on Vascular Dementia (Ljubljana, 16-18th October 2015).
Results
We agreed on key areas that could be of relevance to VaD research: systematic review of existing studies; individual patient level analyses of existing trials and cohorts and linking electronic health record data to other datasets. We illustrated each theme with a case-study of an existing project that has utilised this approach.
Conclusions
There are many opportunities for the VaD research community to make better use of existing data. The volume of potentially available data is increasing and the opportunities for using these resources to progress the VaD research agenda are exciting. Of course, these approaches come with inherent limitations and biases, as bigger datasets are not necessarily better datasets and maintaining rigour and critical analysis will be key to optimising data use.

: http://publica.fraunhofer.de/dokumente/N-484752.html