Universität zu Köln

Welten, Sascha ORCID: 0000-0001-5570-9672, Hempel, Lars, Abedi, Masoud ORCID: 0000-0003-3986-4028, Mou, Yongli ORCID: 0000-0002-2064-0107, Jaberansary, Mehrshad, Neumann, Laurenz, Weber, Sven ORCID: 0000-0002-8518-9097, Tahar, Kais ORCID: 0000-0001-9683-0575, Yediel, Yeliz Ucer, Loebe, Matthias, Decker, Stefan, Beyan, Oya and Kirsten, Toralf (2022). Multi-Institutional Breast Cancer Detection Using a Secure On-Boarding Service for Distributed Analytics. Appl. Sci.-Basel, 12 (9). BASEL: MDPI. ISSN 2076-3417

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Abstract

The constant upward movement of data-driven medicine as a valuable option to enhance daily clinical practice has brought new challenges for data analysts to get access to valuable but sensitive data due to privacy considerations. One solution for most of these challenges are Distributed Analytics (DA) infrastructures, which are technologies fostering collaborations between healthcare institutions by establishing a privacy-preserving network for data sharing. However, in order to participate in such a network, a lot of technical and administrative prerequisites have to be made, which could pose bottlenecks and new obstacles for non-technical personnel during their deployment. We have identified three major problems in the current state-of-the-art. Namely, the missing compliance with FAIR data principles, the automation of processes, and the installation. In this work, we present a seamless on-boarding workflow based on a DA reference architecture for data sharing institutions to address these problems. The on-boarding service manages all technical configurations and necessities to reduce the deployment time. Our aim is to use well-established and conventional technologies to gain acceptance through enhanced ease of use. We evaluate our development with six institutions across Germany by conducting a DA study with open-source breast cancer data, which represents the second contribution of this work. We find that our on-boarding solution lowers technical barriers and efficiently deploys all necessary components and is, therefore, indeed an enabler for collaborative data sharing.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Welten, Sascha UNSPECIFIED orcid.org/0000-0001-5570-9672 UNSPECIFIED Hempel, Lars UNSPECIFIED UNSPECIFIED UNSPECIFIED Abedi, Masoud UNSPECIFIED orcid.org/0000-0003-3986-4028 UNSPECIFIED Mou, Yongli UNSPECIFIED orcid.org/0000-0002-2064-0107 UNSPECIFIED Jaberansary, Mehrshad UNSPECIFIED UNSPECIFIED UNSPECIFIED Neumann, Laurenz UNSPECIFIED UNSPECIFIED UNSPECIFIED Weber, Sven UNSPECIFIED orcid.org/0000-0002-8518-9097 UNSPECIFIED Tahar, Kais UNSPECIFIED orcid.org/0000-0001-9683-0575 UNSPECIFIED Yediel, Yeliz Ucer UNSPECIFIED UNSPECIFIED UNSPECIFIED Loebe, Matthias UNSPECIFIED UNSPECIFIED UNSPECIFIED Decker, Stefan UNSPECIFIED UNSPECIFIED UNSPECIFIED Beyan, Oya UNSPECIFIED UNSPECIFIED UNSPECIFIED Kirsten, Toralf UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-681663
DOI:	10.3390/app12094336
Journal or Publication Title:	Appl. Sci.-Basel
Volume:	12
Number:	9
Date:	2022
Publisher:	MDPI
Place of Publication:	BASEL
ISSN:	2076-3417
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language CLINICAL-TRIAL DATA; MEDICINE; WORKFLOW Multiple languages Chemistry, Multidisciplinary; Engineering, Multidisciplinary; Materials Science, Multidisciplinary; Physics, Applied Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/68166