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dc.contributor.authorHirsch, Sven-
dc.contributor.authorJuchler, Norman-
dc.date.accessioned2019-10-23T14:30:55Z-
dc.date.available2019-10-23T14:30:55Z-
dc.date.issued2019-
dc.identifier.urihttps://digitalcollection.zhaw.ch/handle/11475/18565-
dc.description.abstractClinical data science is an emerging discipline, owing to recent developments in the acquisition, storage and processing of large amounts of clinical data. The increasing wealth of data, however, demands for interdisciplinary collaborations, which imposes new challenges. The need for properly dealing with selection biases and establishing balanced databases becomes a key issue to be addressed in the field of digital health. Pre-existing beliefs about the disease sometimes are badly supported by evidence. Wrong assumptions or selection biases, however, will skew the subsequent analyses and mislead the interpretation of results. Subjective or approximate assessments by clinicians, just like missing/censored information about the patients, the data acquisition process or the pathology under examination make data scientific approaches introduce uncertainty about the data to be processed. All this requires robust approaches and very good domain knowledge to avoid false predictions. To successfully reach the clinically relevant statements calls for transparent methods and efficient tools for creating and communicating insights to practitioners. This talk will present some of the experiences made throughout our research on intracranial aneurysms and discuss how we manage these challenges. It will touch the detection of visual biomarkers with machine learning, present an approach to quantifying rather vaguely defined subjective estimations and demonstrate how visualization helps to detect clinical pathways in high dimensional diagnostic data.de_CH
dc.language.isoende_CH
dc.rightsLicence according to publishing contractde_CH
dc.subject.ddc005: Computerprogrammierung, Programme und Datende_CH
dc.subject.ddc616: Innere Medizin und Krankheitende_CH
dc.titleReal and assumed insights : statistical models and imaging biomarkers for disease characterization of intracranial aneurysmsde_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.details1. Digital Health Lab Day (Life in Numbers 5), Wädenswil, 3. October 2019de_CH
zhaw.funding.euNode_CH
zhaw.originated.zhawYesde_CH
zhaw.publication.statuspublishedVersionde_CH
zhaw.publication.reviewNot specifiedde_CH
zhaw.webfeedBiomedical Simulationde_CH
zhaw.webfeedDatalabde_CH
zhaw.webfeedDigital Health Labde_CH
zhaw.funding.zhawAneuXde_CH
zhaw.author.additionalNode_CH
Appears in Collections:Publikationen Life Sciences und Facility Management

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