Pan-european data harmonization for biobanks in ADOPT BBMRI-ERIC
<p> <b>Background</b> 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...
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| Hauptverfasser: | , , , , , , , , , , , , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2019-11-09
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| In: |
Applied clinical informatics
Year: 2019, Jahrgang: 10, Heft: 04, Pages: 679-692 |
| ISSN: | 1869-0327 |
| DOI: | 10.1055/s-0039-1695793 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1055/s-0039-1695793 Verlag, lizenzpflichtig, Volltext: http://www.thieme-connect.de/DOI/DOI?10.1055/s-0039-1695793 
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| Verfasserangaben: | Sebastian Mate, Marvin Kampf, Wolfgang Rödle, Stefan Kraus, Rumyana Proynova, Kaisa Silander, Lars Ebert, Martin Lablans, Christina Schüttler, Christian Knell, Niina Eklund, Michael Hummel, Petr Holub, Hans-Ulrich Prokosch |
| Zusammenfassung: | <p> <b>Background</b> 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.</p> <p> <b>Objectives</b> 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.</p> <p> <b>Methods</b> 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.</p> <p> <b>Results</b> 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.</p> <p> <b>Conclusion</b> 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.</p> |
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| Beschreibung: | Gesehen am 25.03.2020 |
| Beschreibung: | Online Resource |
| ISSN: | 1869-0327 |
| DOI: | 10.1055/s-0039-1695793 |