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dc.contributor.authorBaig, Mariam
dc.contributor.otherQueen's University (Kingston, Ont.). Theses (Queen's University (Kingston, Ont.))en
dc.date2008-09-27 11:14:04.85en
dc.date.accessioned2008-09-27T15:20:30Z
dc.date.available2008-09-27T15:20:30Z
dc.date.issued2008-09-27T15:20:30Z
dc.identifier.urihttp://hdl.handle.net/1974/1488
dc.descriptionThesis (Master, Computing) -- Queen's University, 2008-09-27 11:14:04.85en
dc.description.abstractA Stroke can affect different parts of the human body depending on the area of brain effected; our research focuses on upper limb motor dysfunction for stroke patients. In current practice, ordinal scale systems are used for conducting physical assessment of upper limb impairment. The reliability of these assessments is questionable, since their coarse ratings cannot reliably distinguish between the different levels of performance. This thesis describes the design, implementation and evaluation of a novel system to facilitate stroke diagnosis which relies on data collected with an innovative KINARM robotic tool. This robotic tool allows for an objective quantification of motor function and performance assessment for stroke patients. The main methodology for the research is Case Based Reasoning (CBR) - an effective paradigm of artificial intelligence that relies on the principle that a new problem is solved by observing similar, previously encountered problems and adapting their known solutions. A CBR system was designed and implemented for a repository of stroke subjects who had an explicit diagnosis and prognosis. For a new stroke patient, whose diagnosis was yet to be confirmed and who had an indefinite prognosis, the CBR model was effectively used to retrieve analogous cases of previous stroke patients. These similar cases provide useful information to the clinicians, facilitating them in reaching a potential solution for stroke diagnosis and also a means to validate other imaging tests and clinical assessments to confirm the diagnosis and prognosis.en
dc.format.extent4912478 bytes
dc.format.mimetypeapplication/pdf
dc.languageenen
dc.language.isoenen
dc.relation.ispartofseriesCanadian thesesen
dc.rightsThis publication is made available by the authority of the copyright owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted by the copyright laws without written authority from the copyright owner.en
dc.subjectCase-based reasoningen
dc.subjectStroke diagnosisen
dc.subjectKINARMen
dc.subjectHealth informaticsen
dc.subjectTA-3en
dc.subjectContext-based similarityen
dc.titleCase-based reasoning - An effective paradigm for providing diagnostic support for stroke patientsen
dc.typethesisen
dc.description.degreeMasteren
dc.contributor.supervisorGlasgow, Janice I.en
dc.contributor.departmentComputingen


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