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Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research.

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Lim Choi Keung, Sarah Niukyun, Zhao, Lei, Rossiter, James, McGilchrist, Mark, Culross, Frank, Ethier, Jean-Francois, Burgun, Anita, Verheij, Robert A., Khan, Nasra, Taweel, Adel, Curcin, Vasa, Delaney, B. and Arvanitis, Theodoros N. (2014) Detailed clinical modelling approach to data extraction from heterogeneous data sources for clinical research. AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science, Volume 2014 . pp. 55-59.

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Abstract

The reuse of routinely collected clinical data for clinical research is being explored as part of the drive to reduce duplicate data entry and to start making full use of the big data potential in the healthcare domain. Clinical researchers often need to extract data from patient registries and other patient record datasets for data analysis as part of clinical studies. In the TRANSFoRm project, researchers define their study requirements via a Query Formulation Workbench. We use a standardised approach to data extraction to retrieve relevant information from heterogeneous data sources, using semantic interoperability enabled via detailed clinical modelling. This approach is used for data extraction from data sources for analysis and for pre-population of electronic Case Report Forms from electronic health records in primary care clinical systems.

Item Type: Journal Article
Divisions: Faculty of Science > WMG (Formerly the Warwick Manufacturing Group)
Journal or Publication Title: AMIA Joint Summits on Translational Science proceedings AMIA Summit on Translational Science
Publisher: American Medical Informatics Association
ISSN: 2153-4063
Official Date: 9 April 2014
Dates:
DateEvent
9 April 2014Published
Volume: Volume 2014
Page Range: pp. 55-59
Status: Not Peer Reviewed
Publication Status: Published
Access rights to Published version: Open Access
Open Access Version:
  • PubMed Central

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