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iDECIDE: An Evidence-based Decision Support System for Improving Postprandial Blood Glucose by Accounting for Patient’s Preferences


Abstract Type 1 diabetes (T1D) is a chronic disease that affects 1.25 million people in the United States. There is no known cure and patients must self-manage the disease to avoid complications resulting from blood glucose (BG) excursions. Patients are more likely to adhere to treatments when they incorporate lifestyle preferences. Current technologies that assist patients fail to consider two factors that are known to affect BG: exercise and alcohol. The hypothesis is postprandial blood glucose levels of adult patients with T1D can be improved by providing insulin bolus or carbohydrate recommendations that account for meal and alcohol carbohydrates, glycemic excursion, and planned exercise. I propose an evidence-based decision support tool, i... (more)
Created Date 2017
Contributor Groat, Danielle (Author) / Grando, Maria Adela (Advisor) / Kaufman, David (Committee member) / Thompson, Bithika (Committee member) / Arizona State University (Publisher)
Subject Artificial intelligence / Behavioral sciences / Endocrinology / alcohol / bolus calculator / clinical decision support / exercise / insulin pump / type 1 diabetes
Type Doctoral Dissertation
Extent 229 pages
Language English
Copyright
Reuse Permissions All Rights Reserved
Note Doctoral Dissertation Biomedical Informatics 2017
Collaborating Institutions Graduate College / ASU Library
Additional Formats MODS / OAI Dublin Core / RIS


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Description Dissertation/Thesis