Author: Grossman, Joshua; Ward, Andrew; Crandell, Jamie L; Prahalad, Priya; Maahs, David M; Scheinker, David
Title: Improved individual and population-level HbA1c estimation using CGM data and patient characteristics. Cord-id: i5wm6ek6 Document date: 2021_5_17
ID: i5wm6ek6
Snippet: Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.
Document: Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.
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