A therapy parameter-based model for predicting blood glucose concentrations in patients with type 1 diabetes

Abstract

c o m p u t e r m e t h o d s a n d p r o g r a m s i n b i o m e d i c i n e 1 1 8 ( 2 0 1 5 ) [107][108][109][110][111][112][113][114][115][116][117][118][119][120][121][122][123] j o u r n a l h o m e p a g e : w w w . i n t l . e l s e v i e r h e a l t h . c o m / j o u r n a l s / c m p b 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.

A hybrid dynamic wavelet-based modeling method for blood glucose concentration prediction in type 1 diabetes

“…[ 32 ] Data simulated by this simulator have been used as a benchmark in numerous papers. [ 18 21 33 34 ] The UVa/Padova simulator model involves several submodels, describing insulin injection, appearance rates of glucose, and meal intake. [ 35 ] Equations of the model are presented in detail.…”

“…Where w(k) = Pen(y(k), ŷ(k)) was described in the study by Del Favero et al [ 48 ] In this work, gRMSE is represented as gFIT = 1 − gRMSE for judging the results, similar to other metrics introduced here. R 2 , another metric for testing the goodness-of-fit, is more sensitive to outliers;[ 34 ] the glucose-weighted form of R 2 is thus calculated as…”

Long-Term Glucose Forecasting Using a Physiological Model and Deconvolution of the Continuous Glucose Monitoring Signal

“…Glucose forecasting in T1D is a relatively mature field with several algorithms having been proposed, and comprehensive and extensive reviews published, which provides a taxonomy of the different types of existing algorithms [6,7,8]. In particular, four main types of approaches were identified depending on the type of model being used: physiological models [9], data-based models [10,11,12], hybrid models [13], and control relevant models [14,15,16]. Another distinction that can be done among the existing algorithms is based on the type of inputs being considered.…”

“…Another distinction that can be done among the existing algorithms is based on the type of inputs being considered. A significant proportion of these algorithms use CGM data as the unique source of information to forecast glucose levels [17], while other algorithms use additional exogenous inputs such as meal intake and insulin doses [9], and only a few of them take into account physical exercise [18]. Furthermore, additional information such as meal absorption information could potentially further improve such accuracy [19].…”

“…Furthermore, additional information such as meal absorption information could potentially further improve such accuracy [19]. Regarding the prediction horizon, most of the algorithms focus on short-term predictions (i.e., <60 min) [20], with fewer works focusing on longer prediction horizons [9,21,22]. Of note, it has been shown that for short prediction horizons (e.g., 30 min), differences in the forecasting accuracy among different algorithms is marginal [23].…”