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dc.contributor.authorGeldof, Tine
dc.contributor.authorVan Damme, Nancy
dc.contributor.authorHuys, Isabelle
dc.contributor.authorVan Dyck, Walter
dc.date.accessioned2020-01-08T15:31:06Z
dc.date.available2020-01-08T15:31:06Z
dc.date.issued2020en_US
dc.identifier.doi10.3389/fphar.2019.01665
dc.identifier.urihttp://hdl.handle.net/20.500.12127/6426
dc.description.abstractLittle research has been done in pharmacoepidemiology on the use of machine learning for exploring medicinal treatment effectiveness in oncology. Therefore, the aim of this study was to explore the added value of machine learning methods to investigate individual treatment responses for glioblastoma patients treated with temozolomide.en_US
dc.language.isoenen_US
dc.publisherFrontiersen_US
dc.subjectReal-world Evidence (RWE)en_US
dc.subjectOncologyen_US
dc.subjectExploratory Studyen_US
dc.subjectPropensity Score Modelingen_US
dc.subjectDecision Treeen_US
dc.subjectMachine Learningen_US
dc.titlePatient-level effectiveness prediction modeling for glioblastoma using classification treesen_US
dc.identifier.journalFrontiers in Pharmacologyen_US
dc.source.volume10
dc.source.issueJanuary
dc.source.beginpage1
dc.source.endpage10
dc.contributor.departmentKU Leuvenen_US
dc.contributor.departmentBelgian Cancer Registry, Belgiumen_US
vlerick.knowledgedomainSpecial Industries : Healthcare Managementen_US
vlerick.typearticleJournal article with impact factoren_US
vlerick.vlerickdepartmentTOMen_US
dc.identifier.vperid176581en_US
dc.identifier.vperid31183en_US


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