Universität zu Köln

Welten, Sascha, Mou, Yongli, Neumann, Laurenz, Jaberansary, Mehrshad, Ucer, Yeliz Yediel, Kirsten, Toralf ORCID: 0000-0001-7117-4268, Decker, Stefan and Beyan, Oya (2022). A Privacy-Preserving Distributed Analytics Platform for Health Care Data. Methods Inf. Med., 61. S. E1 - 11. STUTTGART: GEORG THIEME VERLAG KG. ISSN 2511-705X

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Abstract

Background In recent years, data-driven medicine has gained increasing importance in terms of diagnosis, treatment, and research due to the exponential growth of health care data. However, data protection regulations prohibit data centralisation for analysis purposes because of potential privacy risks like the accidental disclosure of data to third parties. Therefore, alternative data usage policies, which comply with present privacy guidelines, are of particular interest. Objective We aim to enable analyses on sensitive patient data by simultaneously complying with local data protection regulations using an approach called the Personal Health Train (PHT), which is a paradigm that utilises distributed analytics (DA) methods. The main principle of the PHT is that the analytical task is brought to the data provider and the data instances remain in their original location. Methods In this work, we present our implementation of the PHT paradigm, which preserves the sovereignty and autonomy of the data providers and operates with a limited number of communication channels. We further conduct a DA use case on data stored in three different and distributed data providers. Results We show that our infrastructure enables the training of data models based on distributed data sources. Conclusion Our work presents the capabilities of DA infrastructures in the health care sector, which lower the regulatory obstacles of sharing patient data. We further demonstrate its ability to fuel medical science by making distributed data sets available for scientists or health care practitioners.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Welten, Sascha UNSPECIFIED UNSPECIFIED UNSPECIFIED Mou, Yongli UNSPECIFIED UNSPECIFIED UNSPECIFIED Neumann, Laurenz UNSPECIFIED UNSPECIFIED UNSPECIFIED Jaberansary, Mehrshad UNSPECIFIED UNSPECIFIED UNSPECIFIED Ucer, Yeliz Yediel UNSPECIFIED UNSPECIFIED UNSPECIFIED Kirsten, Toralf UNSPECIFIED orcid.org/0000-0001-7117-4268 UNSPECIFIED Decker, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Beyan, Oya UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-680380
DOI:	10.1055/s-0041-1740564
Journal or Publication Title:	Methods Inf. Med.
Volume:	61
Page Range:	S. E1 - 11
Date:	2022
Publisher:	GEORG THIEME VERLAG KG
Place of Publication:	STUTTGART
ISSN:	2511-705X
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language Computer Science, Information Systems; Health Care Sciences & Services; Medical Informatics Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68038