A Privacy-Preserving Distributed Analytics Platform for Health Care Data

Methods of Information in Medicine, vol. 61, pp. e1–e11

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.

Authors 8

1. Sascha Welten Aachen Chair of Computer Science 5

   RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany

2. Yongli Mou Aachen Chair of Computer Science 5

   RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany

3. Laurenz Neumann Aachen Chair of Computer Science 5

   RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany

4. Mehrshad Jaberansary Aachen Chair of Computer Science 5

   RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany

5. Yeliz Yediel Ucer

   Fraunhofer Institute for Applied Information Technology

   Affiliation as printed

   Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany

6. Toralf Kirsten

   University Hospital Leipzig

   Affiliation as printed

   Department of Medical Data Science, University Medical Center Leipzig, Leipzig, Germany

7. Stefan Decker Aachen Chair of Computer Science 5

   Fraunhofer Institute for Applied Information Technology · RWTH Aachen University

   Affiliation as printed

   Chair of Computer Science 5, RWTH Aachen University, Aachen, Germany

   Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany

8. Oya Beyan

   University of Cologne · Fraunhofer Institute for Applied Information Technology · University Hospital Cologne

   Affiliation as printed

   Department of Data Science and Artificial Intelligence, Fraunhofer FIT, Sankt Augustin, Germany

   Institute for Medical Informatics, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany