Please use this identifier to cite or link to this item: https://scidar.kg.ac.rs/handle/123456789/12729
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dc.contributor.authorSakellarios, Antonis-
dc.contributor.authorCorreia J.-
dc.contributor.authorKyriakidis S.-
dc.contributor.authorGeorga E.-
dc.contributor.authorTachos N.-
dc.contributor.authorSiogkas, Panagiotis-
dc.contributor.authorSans F.-
dc.contributor.authorStofella P.-
dc.contributor.authorMassimiliano V.-
dc.contributor.authorClemente, Alberto-
dc.contributor.authorRocchiccioli S.-
dc.contributor.authorPelosi, Gualtiero-
dc.contributor.authorFilipovic, Nenad-
dc.contributor.authorFotiadis D.-
dc.date.accessioned2021-04-20T21:34:44Z-
dc.date.available2021-04-20T21:34:44Z-
dc.date.issued2020-09-13-
dc.identifier.issn17517575-
dc.identifier.urihttps://scidar.kg.ac.rs/handle/123456789/12729-
dc.description.abstract© 2020 Informa UK Limited, trading as Taylor & Francis Group. We present the architecture and the usability testing of a novel cloud-based platform, which integrates cyber-physical systems and interoperability standards enabling a clinical decision support system for risk stratification, diagnosis, prognosis and treatment of CAD. In this work multi-disciplinary human data were used for the development of machine learning and computational biomechanics based predictive models. Two Lab-on-Chip devices have been integrated into the cloud platform. A targeted RNA-panel provides the mRNA gene expression values for the stratification algorithm. The results of the usability testing demonstrate that the platform is efficient, accurate and performs all developed tasks quickly.-
dc.relation.ispartofEnterprise Information Systems-
dc.titleA cloud-based platform for the non-invasive management of coronary artery disease-
dc.typearticle-
dc.identifier.doi10.1080/17517575.2020.1746975-
dc.identifier.scopus85083557430-
Appears in Collections:Faculty of Engineering, Kragujevac

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