Personalized glucose forecasting for type 2 diabetes using data assimilation.

ABSTRACT: Type 2 diabetes leads to premature death and reduced quality of life for 8% of Americans. Nutrition management is critical to maintaining glycemic control, yet it is difficult to achieve due to the high individual differences in glycemic response to nutrition. Anticipating glycemic impact of different meals can be challenging not only for individuals with diabetes, but also for expert diabetes educators. Personalized computational models that can accurately forecast an impact of a given meal on an individual's blood glucose levels can serve as the engine for a new generation of decision support tools for individuals with diabetes. However, to be useful in practice, these computational engines need to generate accurate forecasts based on limited datasets consistent with typical self-monitor

SUBMITTER: Albers DJ 

PROVIDER: S-EPMC5409456 | biostudies-literature | 2017 Apr

REPOSITORIES: biostudies-literature