Comput Methods Programs Biomed - A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes.

Tópicos

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{ model(2656) set(1616) predict(1553) }
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Resumo

In this paper, the problem of predicting blood glucose concentrations (BG) for the treatment of patients with type 1 diabetes, is addressed. Predicting BG is of very high importance as most treatments, which consist in exogenous insulin injections, rely on the availability of BG predictions. Many models that can be used for predicting BG are available in the literature. However, it is widely admitted that it is almost impossible to perfectly model blood glucose dynamics while still being able to identify model parameters using only blood glucose measurements. The main contribution of this work is to propose a simple and identifiable linear dynamical model, which is based on the static prediction model of standard therapy. It is shown that the model parameters are intrinsically correlated with physician-set therapy parameters and that the reduction of the number of model parameters to identify leads to inferior data fits but to equivalent or slightly improved prediction capabilities compared to state-of-the-art models: a sign of an appropriate model structure and superior reliability. The validation of the proposed dynamic model is performed using data from the UVa simulator and real clinical data, and potential uses of the proposed model for state estimation and BG control are discussed.

Resumo Limpo

paper problem predict blood glucos concentr bg treatment patient type diabet address predict bg high import treatment consist exogen insulin inject reli avail bg predict mani model can use predict bg avail literatur howev wide admit almost imposs perfect model blood glucos dynam still abl identifi model paramet use blood glucos measur main contribut work propos simpl identifi linear dynam model base static predict model standard therapi shown model paramet intrins correl physicianset therapi paramet reduct number model paramet identifi lead inferior data fit equival slight improv predict capabl compar stateoftheart model sign appropri model structur superior reliabl valid propos dynam model perform use data uva simul real clinic data potenti use propos model state estim bg control discuss

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