Remote medical monitoring decision support system and user interface usabilityPublic Deposited
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User requirements were gathered from home care clinicians to understand what parameters are critical in monitoring the health of home care clients. Once solicited, the most appropriate graphical user interface (GUI) features were determined in order to conduct a usability test and qualitative analysis of two GUI prototypes. A few key findings include the need to display data trends, client personal targets and alerts for emergent situations.Visual data mining combines data visualization and data mining. Therefore, in order to populate the GUI with home care clients' data and support decision making, data mining was also explored. Two data mining techniques, a segmentation algorithm and a feed-forward neural network, were evaluated for their ability to detect trends from simulated data. Results indicate that the segmentation algorithm is more accurate with the given data sets but the network is more robust with varying levels of noise.
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- Copyright © 2011 the author(s). Theses may be used for non-commercial research, educational, or related academic purposes only. Such uses include personal study, research, scholarship, and teaching. Theses may only be shared by linking to Carleton University Institutional Repository and no part may be used without proper attribution to the author. No part may be used for commercial purposes directly or indirectly via a for-profit platform; no adaptation or derivative works are permitted without consent from the copyright owner.
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