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

Abstract

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.

A Review of AI and Data Science Support for Cancer Management

“…Use of controlled vocabularies such as SNOMED CT and LOINC facilitates the agreement. Similar to the terminology-suggestion system of Ulrich et al [61] described in Section 3.2.1, Mate et al [68] support term matching further via an algorithm that supports fuzzy matching of terms, utilization of synonyms, and sentiment tagging to suggest mapping of SDEs to CDEs. A final step of data alignment replaces the source value sets with those from the target terminology, as well as converts between different data types according to expert-curated mapping rules.…”

“…The data integration approaches from recent years use a combination of both approaches. Several papers [46][47][48][58][59][60] present ETL database functions that allow pulling data from a source database and placing it into a target database. The first three of these papers define manual processes by which local sites define mappings between source data elements (SDE) to a set of needed common data elements (CDE).…”

A Multi-User Terminology Mapping Toolbox

“…We recently developed a semi-automatic mapping approach within the ADOPT BBMRI-ERIC biobank networking project [13] and considered using it for the LOINC mapping. We updated the matching component with multi-threading capabilities and an improved matching algorithm to handle the very large LOINC terminology, but initial matching results were not satisfactory.…”

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“…Более десяти лет назад зарубежные коллеги, работающие в области биобанкирования, поставили перед собой задачу создать общую инфраструктуру для совместного использования всех доступных ресурсов биобанков [1,2]. Практически сразу возник вопрос о гармонизации работы биобанков [3] и создания их единого реестра [4]. В это же время регио нальными и международными организациями начали разрабатываться первые глоссарии.…”