Activity Number:
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178
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Type:
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Contributed
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Date/Time:
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Monday, August 1, 2016 : 10:30 AM to 12:20 PM
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Sponsor:
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Biopharmaceutical Section
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Abstract #318888
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Title:
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A Bayesian Approach for Classification Based on Continuous Glucose Monitoring Data of Diabetic Patients
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Author(s):
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Hui Zheng*
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Companies:
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Harvard Medical School
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Keywords:
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Bayesian ;
Spline ;
classification ;
continuous glucose monitoring ;
diabetes ;
CGM
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Abstract:
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We propose a Bayesian modeling approach to distinguish subgroups of diabetic patients based on their glucose profiles. The glucose data are collected using continuous glucose monitoring (CGM) devices which measure glucose at a frequency of once 1 to 5 minutes. The classification method is based on a hierarchical model using B-spline basis functions to smooth the glucose measurements while allowing for auto-correlations. In the training phase, the mean and variance-covariance matrices of glucose profiles are obtained for each subgroup. The classification is based the posterior probabilities for a patient to belong to each subgroup. We illustrate the method using CGM data collected in a clinical trial .
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Authors who are presenting talks have a * after their name.
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