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Empirical linear dynamic models have been identified from ambulatory data from two type 1 diabetes subjects in order to determine approximately how far into the future the models could be expected to make reasonably accurate predictions. For a prediction horizon of 30 minutes, FIT values (related to R2 values) of the model predictions for validation data were 46% for one subject and 60% for the other...
There is a significant push to develop closed-loop control systems to deliver insulin for type 1 diabetic subjects. As part of this process, mathematical models are required to test and validate the proposed algorithms. There are several published physiology-based models of glucose and insulin dynamics in the literature, however, all of them were derived using data from subjects without diabetes....
A dual-rate Kalman filter is developed for realtime continuous glucose monitoring. Frequent (5 minute) sampling of a noisy, continuous glucose sensor is used for estimation of glucose and its rate-of-change. Infrequent (8 hour intervals) reference glucose meter samples enable the sensor gain and its rate-of-change to be updated. The dual-rate Kalman filter formulation accounts for uncertainty in both...
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