Glycemic-aware metrics and oversampling techniques for predicting blood glucose levels using machine learning.

ABSTRACT: Techniques using machine learning for short term blood glucose level prediction in patients with Type 1 Diabetes are investigated. This problem is significant for the development of effective artificial pancreas technology so accurate alerts (e.g. hypoglycemia alarms) and other forecasts can be generated. It is shown that two factors must be considered when selecting the best machine learning technique for blood glucose level regression: (i) the regression model performance metrics being used to select the model, and (ii) the preprocessing techniques required to account for the imbalanced time spent by patients in different portions of the glycemic range. Using standard benchmark data, it is demonstrated that different regression model/preprocessing technique combinations exhibit different

SUBMITTER: Mayo M 

PROVIDER: S-EPMC6886807 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature