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Improved individual and population-level HbA1c estimation using CGM data and patient characteristics
Joshua Grossman1, Andrew Ward1, Jamie L Crandell2
1Department of Management Science and Engineering, Stanford School of Engineering, Stanford, CA, USA.
Journal of Diabetes and Its Complications
|June 15, 2021
Abstract:
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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