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Pan-European Data Harmonization for Biobanks in ADOPT BBMRI-ERIC

Background  High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. T...

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Detalles Bibliográficos
Autores principales: Mate, Sebastian, Kampf, Marvin, Rödle, Wolfgang, Kraus, Stefan, Proynova, Rumyana, Silander, Kaisa, Ebert, Lars, Lablans, Martin, Schüttler, Christina, Knell, Christian, Eklund, Niina, Hummel, Michael, Holub, Petr, Prokosch, Hans-Ulrich
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Georg Thieme Verlag KG 2019
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6739205/
https://www.ncbi.nlm.nih.gov/pubmed/31509880
http://dx.doi.org/10.1055/s-0039-1695793
Descripción
Sumario:Background  High-quality clinical data and biological specimens are key for medical research and personalized medicine. The Biobanking and Biomolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC) aims to facilitate access to such biological resources. The accompanying ADOPT BBMRI-ERIC project kick-started BBMRI-ERIC by collecting colorectal cancer data from European biobanks. Objectives  To transform these data into a common representation, a uniform approach for data integration and harmonization had to be developed. This article describes the design and the implementation of a toolset for this task. Methods  Based on the semantics of a metadata repository, we developed a lexical bag-of-words matcher, capable of semiautomatically mapping local biobank terms to the central ADOPT BBMRI-ERIC terminology. Its algorithm supports fuzzy matching, utilization of synonyms, and sentiment tagging. To process the anonymized instance data based on these mappings, we also developed a data transformation application. Results  The implementation was used to process the data from 10 European biobanks. The lexical matcher automatically and correctly mapped 78.48% of the 1,492 local biobank terms, and human experts were able to complete the remaining mappings. We used the expert-curated mappings to successfully process 147,608 data records from 3,415 patients. Conclusion  A generic harmonization approach was created and successfully used for cross-institutional data harmonization across 10 European biobanks. The software tools were made available as open source.