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Please use this identifier to cite or link to this item: http://hdl.handle.net/1974/1488

Title: Case-based reasoning - An effective paradigm for providing diagnostic support for stroke patients
Authors: Baig, Mariam

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Keywords: Case-based reasoning
Stroke diagnosis
Health informatics
Context-based similarity
Issue Date: 2008
Series/Report no.: Canadian theses
Abstract: A 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.
Description: Thesis (Master, Computing) -- Queen's University, 2008-09-27 11:14:04.85
URI: http://hdl.handle.net/1974/1488
Appears in Collections:Queen's Graduate Theses and Dissertations
School of Computing Graduate Theses

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