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A distributed analytics platform to execute FHIR-based phenotyping algorithms

: Karim, M.R.; Nguyen, B.-P.; Zimmermann, L.; Kirsten, T.; Löbe, M.; Meineke, F.; Stenzhorn, H.; Kohlbacher, O.; Decker, S.; Beyan, O.

Fulltext ()

Baker, C.J.O.:
SWAT4LS 2018, Semantic Web Applications and Tools for Health Care and Life Sciences. Online resource : Proceedings of the 11th International Conference Semantic Web Applications and Tools for Life Sciences (SWAT4HCLS 2018), Antwerp, Belgium, December 3-6, 2018
Antwerp: CEUR, 2018 (CEUR Workshop Proceedings 2275)
ISSN: 1613-0073
URN: urn:nbn:de:0074-2275-1
Paper 8, 10 pp.
International Conference Semantic Web Applications and Tools for Life Sciences (SWAT4LS) <11, 2018, Antwerp>
Conference Paper, Electronic Publication
Fraunhofer FIT ()

Despite the benefits of reusing health data collected in routine
care, sharing datasets outside of the organizational boundaries is
not always possible due to the legal and ethical restrictions. The Personal
Health Train (PHT) is a novel privacy-preserving approach to execute
analytics tasks at distributed data repositories, without sharing the
data itself. In this work, we report a proof-of-concept implementation of
the PHT by using FHIR data standards and Clinical Query Language
(CQL). The Semantic Web and containerization technologies have been
utilized to move computations to the data. We developed tools to design
phenotyping algorithms on the data consumer side, implemented
an infrastructure to transfer and execute Docker containers at the data
centers, and to return results to the consumers. We experimented the
evaluated PHT infrastructure and tools by designing a phenotyping algorithm
for diabetes mellitus and prostate cancer risk case-control study
and executed it at three distributed FHIR repositories.