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- W2887749376 abstract "In this work, an adaptive and personalized compartment model that translates the abrupt bolus and discrete basal changes into estimates of plasma insulin concentration (PIC) is integrated with a recursive subspace-based system identification approach to characterize the transient dynamics of glycemic measurements. The PIC estimates are obtained through a compartment model with time-varying parameters and obscure carbohydrate consumption effects simultaneously estimated using a filtering algorithm. The estimated PIC is subsequently employed in the recursive system identification approach to determine a linear, time-varying model for predicting glucose measurements. Simulation case studies demonstrate the improvement in prediction ability of the proposed approach (mean absolute error [MAE] of 9.29 mg/dL) compared to the standard method (MAE of 10.19 mg/dL)." @default.
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- W2887749376 date "2018-06-01" @default.
- W2887749376 modified "2023-10-17" @default.
- W2887749376 title "Integrating Compartment Models with Recursive System Identification" @default.
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- W2887749376 doi "https://doi.org/10.23919/acc.2018.8431822" @default.
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