Improved individual and population-level HbA1c estimation using CGM data and patient characteristics

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...

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Detalles Bibliográficos
Autores principales: Grossman, Joshua, Ward, Andrew, Crandell, Jamie L., Prahalad, Priya, Maahs, David M., Scheinker, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier Inc. 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316291/
https://www.ncbi.nlm.nih.gov/pubmed/34127370
http://dx.doi.org/10.1016/j.jdiacomp.2021.107950

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8316291/
https://www.ncbi.nlm.nih.gov/pubmed/34127370
http://dx.doi.org/10.1016/j.jdiacomp.2021.107950

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