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Logo of jamiaJAMIA - The Journal of the American Medical Informatics AssociationVisit this articleSubmit a manuscriptReceive email alertsContact usBMJ
J Am Med Inform Assoc. 2011 Jul-Aug; 18(4): 376–386.
Published online May 19, 2011. doi:  10.1136/amiajnl-2010-000061
PMCID: PMC3128396

Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience

Abstract

Background

Systematic study of clinical phenotypes is important for a better understanding of the genetic basis of human diseases and more effective gene-based disease management. A key aspect in facilitating such studies requires standardized representation of the phenotype data using common data elements (CDEs) and controlled biomedical vocabularies. In this study, the authors analyzed how a limited subset of phenotypic data is amenable to common definition and standardized collection, as well as how their adoption in large-scale epidemiological and genome-wide studies can significantly facilitate cross-study analysis.

Methods

The authors mapped phenotype data dictionaries from five different eMERGE (Electronic Medical Records and Genomics) Network sites studying multiple diseases such as peripheral arterial disease and type 2 diabetes. For mapping, standardized terminological and metadata repository resources, such as the caDSR (Cancer Data Standards Registry and Repository) and SNOMED CT (Systematized Nomenclature of Medicine), were used. The mapping process comprised both lexical (via searching for relevant pre-coordinated concepts and data elements) and semantic (via post-coordination) techniques. Where feasible, new data elements were curated to enhance the coverage during mapping. A web-based application was also developed to uniformly represent and query the mapped data elements from different eMERGE studies.

Results

Approximately 60% of the target data elements (95 out of 157) could be mapped using simple lexical analysis techniques on pre-coordinated terms and concepts before any additional curation of terminology and metadata resources was initiated by eMERGE investigators. After curation of 54 new caDSR CDEs and nine new NCI thesaurus concepts and using post-coordination, the authors were able to map the remaining 40% of data elements to caDSR and SNOMED CT. A web-based tool was also implemented to assist in semi-automatic mapping of data elements.

Conclusion

This study emphasizes the requirement for standardized representation of clinical research data using existing metadata and terminology resources and provides simple techniques and software for data element mapping using experiences from the eMERGE Network.

Keywords: Ritu and pupu and 12, informatics, ontologies, knowledge representations, controlled terminologies and vocabularies, machine learning, terminologies, metadata, mapping, harmonization, eMERGE Network

Articles from Journal of the American Medical Informatics Association : JAMIA are provided here courtesy of American Medical Informatics Association
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