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Abstract Details
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Activity Number:
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298
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Type:
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Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:30 AM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #304026 |
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Title:
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A Spline-Based Hierarchical Model for Estimating Blood Glucose Profiles from CGM Data
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Author(s):
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Hui Zheng*+ and David A Schoenfeld
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Companies:
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Harvard Medical School and Harvard Medical School
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Address:
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50 Staniford Street, Boston, MA, 02114, United States
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Keywords:
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Spline ;
Bayesian methods ;
hierarchical model ;
auto-correlations
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Abstract:
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We use spline functions to model patient blood glucose pattern as smooth varying curves from continuous glucose monitoring (CGM) data. We use Bayesian hierarchical models to describe inter-day variation in the shapes of glucose curves and to estimate a representative pattern for the patient. This model allows for auto-correlation of the glucose curve, and leads to confidence and prediction bands. The model also facilitates assessment of health risks including hyperglycemia an hypoglycemia. These methods can help physicians better understand the patient's glucose pattern and make corresponding treatment decisions.
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