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XplOit: An ontology-based data integration platform supporting the development of predictive models for personalized medicine

: Weiler, G.; Schwarz, U.; Rauch, J.; Rohm, K.; Lehr, T.; Theobald, S.; Kiefer, S.; Götz, K.; Och, K.; Pfeifer, N.; Handl, L.; Smola, S.; Ihle, M.; Turki, A.T.; Beelen, D.W.; Rissland, J.; Bittenbring, J.; Graf, N.


Ugon, A. ; European Federation of Medical Informatics -EFMI-:
Building continents of knowledge in oceans of data : The future of co-created eHealth. Proceedings of MIE 2018
Amsterdam: IOS Press, 2018 (Studies in health technology and informatics 247)
ISBN: 1-61499-852-3
ISBN: 978-1-61499-852-5
ISBN: 978-1-61499-851-8
Medical Informatics Europe Conference (MIE) <2018, Gothenburg>
Fraunhofer IBMT ()

Predictive models can support physicians to tailor interventions and treatments to their individual patients based on their predicted response and risk of disease and help in this way to put personalized medicine into practice. In allogeneic stem cell transplantation risk assessment is to be enhanced in order to respond to emerging viral infections and transplantation reactions. However, to develop predictive models it is necessary to harmonize and integrate high amounts of heterogeneous medical data that is stored in different health information systems. Driven by the demand for predictive instruments in allogeneic stem cell transplantation we present in this paper an ontology-based platform that supports data owners and model developers to share and harmonize their data for model development respecting data privacy.