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dc.contributor.authorSabovcik, Frantisek
dc.contributor.authorCauwenberghs, Nicholas
dc.contributor.authorKouznetsov, Dmitry
dc.contributor.authorHaddad, Francois
dc.contributor.authorAlonso-Betanzos, Amparo
dc.contributor.authorKuznetsova, Tatiana
dc.contributor.authorVens, Celine
dc.date.accessioned2022-03-01T13:19:54Z
dc.date.available2022-03-01T13:19:54Z
dc.date.issued2021
dc.identifier.issn2047-2404
dc.identifier.otherWOS:000701593100025
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/39184
dc.sourceWOS
dc.titleApplying machine learning to detect early stages of cardiac remodelling and dysfunction
dc.typeJournal article
dc.contributor.imecauthorKouznetsov, Dmitry
dc.contributor.imecauthorVens, Celine
dc.contributor.orcidextSabovcik, Frantisek::0000-0001-8275-6431
dc.contributor.orcidextCauwenberghs, Nicholas::0000-0001-8059-7692
dc.contributor.orcidextAlonso-Betanzos, Amparo::0000-0003-0950-0012
dc.contributor.orcidimecKouznetsov, Dmitry::0000-0001-8440-742X
dc.contributor.orcidimecVens, Celine::0000-0003-0983-256X
dc.identifier.doi10.1093/ehjci/jeaa135
dc.source.numberofpages10
dc.source.peerreviewyes
dc.source.beginpage1208
dc.source.endpage1217
dc.source.journalEUROPEAN HEART JOURNAL-CARDIOVASCULAR IMAGING
dc.identifier.pmidMEDLINE:32588036
dc.source.issue10
dc.source.volume22
imec.availabilityPublished - imec


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