Article
NFDI4Health workflow and service for synthetic data generation, assessment and risk management
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Authors
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Sobhan Moazemi
- Fraunhofer SCAI, Sankt Augustin, Germany
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Tim Adams
- Fraunhofer SCAI, Sankt Augustin, Germany
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Hwei Geok Ng
- Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing SCAI, Sankt Augustin, Germany
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Lisa Kühnel
- Knowledge Management, ZB MED – Information Centre for Life Sciences, Köln, Germany; Graduate School DILS, Bielefeld Institute for Bioinformatics Infrastructure (BIBI), Faculty of Technology, Bielefeld University, Bielefeld, Germany
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Julian Schneider
- Knowledge Management, ZB MED – Information Centre for Life Sciences, Köln, Germany
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Anatol-Fiete Näher
- Digital Global Public Health, Hasso Plattner Institute, University of Potsdam, Potsdam, Germany; Institute of Medical Informatics, Charité – Universitätsmedizin, Berlin, Germany; Method Development, Research Infrastructure, and Information Technology, Robert Koch-Institute, Berlin, Germany
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Juliane Fluck
- ZB MED Informationszentrum Lebenswissenschaften, Bonn, Germany; The Agricultural Faculty, University of Bonn, Bonn, Germany; Institute for Geodesy and Geoinformation, University of Bonn, Bonn, Germany
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Holger Fröhlich
- Fraunhofer SCAIUniversität Bonn, Sankt Augustin, Germany; Bonn-Aachen International Center for IT, University of Bonn, Bonn, Germany
| Published: | September 6, 2024 |
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Outline
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Individual health data is crucial for scientific advancements, particularly in developing Artificial Intelligence (AI); however, sharing real patient information is often restricted due to privacy concerns. A promising solution to this challenge is synthetic data generation. This technique creates entirely new datasets that mimic the statistical properties of real data, while preserving confidential patient information. In this paper, we present the workflow and different services developed in the context of Germany’s National Data Infrastructure project NFDI4Health. First, two state-of-the-art AI tools (namely, VAMBN and MultiNODEs) for generating synthetic health data are outlined. Further, we introduce SYNDAT (a public web-based tool) which allows users to visualize and assess the quality and risk of synthetic data provided by desired generative models. Additionally, the utility of the proposed methods and the web-based tool is showcased using data from Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Center for Cancer Registry Data of the Robert Koch Institute (RKI).
The authors declare that they have no competing interests.
The authors declare that an ethics committee vote is not required.
