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A Statistical Clinical Decision Support Tool for Determining Thresholds in Remote Monitoring Using Predictive Analytics


Abstract Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was predictably validated to .80 Generalized R-square, demonstrating the adequate learning... (more)
Created Date 2013
Contributor Fralick, Celeste Rachelle (Author) / Muthuswamy, Jitendran (Advisor) / O'Shea, Terrance (Advisor) / Labelle, Jeffrey (Committee member) / Pizziconi, Vincent (Committee member) / Shea, Kimberly (Committee member) / Arizona State University (Publisher)
Subject Biomedical engineering / Medicine / Statistics / Clinical Decision Suppport Tool / COPD / Machine learning / Predictive Analytics / Statistical Process Control / Telemonitoring
Type Doctoral Dissertation
Extent 174 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Ph.D. Engineering 2013
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis