Model-Fusion-Based Online Glucose Concentration Predictions in People with Type 1 Diabetes.

ABSTRACT: Accurate predictions of glucose concentrations are necessary to develop an artificial pancreas (AP) system for people with type 1 diabetes (T1D). In this work, a novel glucose forecasting paradigm based on a model fusion strategy is developed to accurately characterize the variability and transient dynamics of glycemic measurements. To this end, four different adaptive filters and a fusion mechanism are proposed for use in the online prediction of future glucose trajectories. The filter fusion mechanism is developed based on various prediction performance indexes to guide the overall output of the forecasting paradigm. The efficiency of the proposed model fusion based forecasting method is evaluated using simulated and clinical datasets, and the results demonstrate the capability and predi

SUBMITTER: Yu X 

PROVIDER: S-EPMC5736323 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature