János Bolyai Research Scholarship of the Hungarian Academy of Sciences(TKP2021-NVA-15)
(TKP2021-EGA-2) Támogató: NKFIH
(OTKA139010) Támogató: Hungarian National Research, Development and Innovation Office
Fragmentation of health data and biomedical research data is a major obstacle for
precision medicine based on data-driven decisions. The development of personalized
medicine requires the efficient exploitation of health data resources that are extraordinary
in size and complexity, but highly fragmented, as well as technologies that enable
data sharing across institutions and even borders. Biobanks are both sample archives
and data integration centers. The analysis of large biobank data warehouses in federated
datasets promises to yield conclusions with higher statistical power. A prerequisite
for data sharing is harmonization, i.e., the mapping of the unique clinical and molecular
characteristics of samples into a unified data model and standard codes. These databases,
which are aligned to a common schema, then make healthcare information available for
privacy-preserving federated data sharing and learning. The re-evaluation of sensitive
health data is inconceivable without the protection of privacy, the legal and conceptual
framework for which is set out in the GDPR (General Data Protection Regulation) and
the FAIR (findable, accessible, interoperable, reusable) principles. For biobanks
in Europe, the BBMRI-ERIC (Biobanking and Biomolecular Research Infrastructure - European
Research Infrastructure Consortium) research infrastructure develops common guidelines,
which the Hungarian BBMRI Node joined in 2021. As the first step, a federation of
biobanks can connect fragmented datasets, providing high-quality data sets motivated
by multiple research goals. Extending the approach to real-word data could also allow
for higher level evaluation of data generated in the real world of patient care, and
thus take the evidence generated in clinical trials within a rigorous framework to
a new level. In this publication, we present the potential of federated data sharing
in the context of the Semmelweis University Biobanks joint project. Orv Hetil. 2023;
164(21): 811-819.
