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dc.contributor.authorJuchler, Norman-
dc.contributor.authorSchilling, Sabine-
dc.contributor.authorWatanabe, Kazuhiro-
dc.contributor.authorAnzai, Hitomi-
dc.contributor.authorRüfenacht, Daniel-
dc.contributor.authorBijlenga, Philippe-
dc.contributor.authorKurtcuoglu, Vartan-
dc.contributor.authorHirsch, Sven-
dc.date.accessioned2018-10-24T08:47:03Z-
dc.date.available2018-10-24T08:47:03Z-
dc.date.issued2018-10-04-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/12078-
dc.description.abstractAn ever increasing amount of medical data is collected and used for scientific and clinical purposes. To benefit from the abundance of data, however, one has to deal with several challenges. The diversity of data sources, the variability seen in the biological systems and the biases and distortions inherent in the acquired data request for robust and flexible data processing pipelines. Here, we illustrate some of these challenges at the hand of our research on intracranial aneurysms and share insights how to deal with these challenges. We present the AneuX AneurysmDataBase. It stores data acquired at multiple clinical centers, supports heterogeneous data (clinical data, imaging data, genetic data, morphological and histological data, etc.) and is aimed for use in both scientific and industrial contexts. We further present five scientific studies that demonstrate the usage of the AneurysmDataBase. In the first application, we evaluated the PHASES score, a recent scoring scheme to guide the clinicians whether to treat an unruptured intracranial aneurysm. We further examined and improved existing morphological descriptors with the goal to associate aneurysm shape with its disease status. In a third study, we quantified the qualitative rating of aneurysm shape by humans. A fourth study aims at inferring information about the disease directly from imaging data by means of convolutional neural nets. Finally, we sketch how to query aneurysms with similar anatomical and morphological properties from a database. With our work, we demonstrate how clinical data sharing can be used for quantitative analyses of aneurysm properties and for the development of diagnostic and prognostic tools.de_CH
dc.language.isoende_CH
dc.rightsNot specifiedde_CH
dc.subjectIntracranial aneurysmsde_CH
dc.subjectClinical data sharingde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc610: Medizin und Gesundheitde_CH
dc.titleClinical data sharing : a data scientist's perspectivede_CH
dc.typeKonferenz: Sonstigesde_CH
dcterms.typeTextde_CH
zhaw.departementLife Sciences und Facility Managementde_CH
zhaw.organisationalunitInstitut für Angewandte Simulation (IAS)de_CH
zhaw.conference.detailsLife in Numbers 4, ZHAW, Waedenswil, 4 October 2018de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statusacceptedVersionde_CH
zhaw.publication.reviewKeine Begutachtungde_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.funding.zhawAneuXde_CH
Appears in Collections:Publikationen Life Sciences und Facility Management

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