Multidimensional Detection Of Outliers In Clinical Registers

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but.event.date26.04.2018cs
but.event.titleStudent EEICT 2018cs
dc.contributor.authorŠiška, Branislav
dc.date.accessioned2019-03-04T10:05:45Z
dc.date.available2019-03-04T10:05:45Z
dc.date.issued2018cs
dc.description.abstractIncorrect data in clinical registers can lead to inaccurate or wrong results. This project is aimed at monitoring and evaluation of data in clinical registers. Usual methods to identify incorrect data are one-dimensional statistical methods per each variable in the register. Proposed method finds outliers in data using machine learning combined with multidimensional statistical methods that transform all column variables of clinical register to one, representing one record of a patient in the register.en
dc.formattextcs
dc.format.extent279-281cs
dc.format.mimetypeapplication/pdfen
dc.identifier.citationProceedings of the 24th Conference STUDENT EEICT 2018. s. 279-281. ISBN 978-80-214-5614-3cs
dc.identifier.isbn978-80-214-5614-3
dc.identifier.urihttp://hdl.handle.net/11012/138238
dc.language.isoskcs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 24th Conference STUDENT EEICT 2018en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.rights.accessopenAccessen
dc.subjectclinical registeren
dc.subjectdetection of outliersen
dc.subjectdata frauden
dc.subjectmachine learningen
dc.titleMultidimensional Detection Of Outliers In Clinical Registersen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs

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