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- W1857366857 abstract "Diabetes Mellitus is a chronic disease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, leading to serious health damages. The therapy is essentially based on insulin injections and depends strongly on patient daily decisions, being mainly based upon empirical experience and rules of thumb. This chapter presents work on data-driven glucose metabolism modeling and short-term, that is, up to 120 min, blood-glucose prediction in Type 1 Diabetes Mellitus (T1DM) subjects. Low-order, individualized, data-driven, stable, physiologically relevant models were identified from a population of 9 datasets from T1DM patients. Model structures include: autoregressive moving average with exogenous inputs (ARMAX) models and state-space models. The performances of the model-based predictors were compared to those achieved by the zero-order hold (ZOH)." @default.
- W1857366857 created "2016-06-24" @default.
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- W1857366857 date "2014-01-01" @default.
- W1857366857 modified "2023-10-18" @default.
- W1857366857 title "Linear Modeling and Prediction in Diabetes Physiology" @default.
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- W1857366857 doi "https://doi.org/10.1007/978-3-642-54464-4_9" @default.
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