Real-Time Adaptive Models for the Personalized Prediction of Glycemic Profile in Type 1 Diabetes Patients

Daskalaki, Elena; Prountzou, Aikaterini; Diem, Peter; Mougiakakou, Stavroula

doi:10.1089/dia.2011.0093

Cited by 67 publications

(67 citation statements)

References 21 publications

(15 reference statements)

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“…-autoregressive with exogenous input models exploiting information on CHO and insulin therapy 11,12 -autoregressive with moving average with exogenous inputs models accounting for food intake, physical activity, emotional stimuli, and lifestyle; 13 physical activity and insulin on board information; 14 and insulin and CHO information 15 -latent variable-based predictors 16 -random forests, support vector-based algorithms, and…”

mentioning

confidence: 99%

“…Gaussian processes using a variety of inputs, such as glucose history, time of the day, plasma insulin concentration, effect of food intake, and energy expenditure [17][18][19] -neural networks using insulin and CHO information; 15 self-monitoring of blood glucose (SMBG) readings; information on insulin, CHO, and hypo-and hyperglycemic symptoms; lifestyle, activity, and emotions; 20 and information on CHO only 21,22 None of these studies systematically evaluated how much each individual input can improve the prediction of glucose concentration.…”

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confidence: 99%

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How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

Zecchin

Facchinetti

Sparacino

et al. 2016

J Diabetes Sci Technol

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Background: In type 1 diabetes (T1D) management, short-term glucose prediction can allow to anticipate therapeutic decisions when hypo/hyperglycemia is imminent. Literature prediction methods mainly use past continuous glucose monitoring (CGM) readings. Sophisticated algorithms can use information on insulin delivered and meal carbohydrate (CHO) content. The quantification of how much insulin and CHO information improves glucose prediction is missing in the literature and is investigated, in an open-loop setting, in this proof-of-concept study. Methods: We adopted a versatile literature prediction methodology able to utilize a variety of inputs. We compared predictors that use (1) CGM; (2) CGM and insulin; (3) CGM and CHO; and (4) CGM, insulin, and CHO. Data of 15 T1D subjects in open-loop setup were used. Prediction was evaluated via absolute error and temporal gain focusing on meal/night periods. The relative importance of each individual input of the predictor was evaluated with a sensitivity analysis. Results: For a prediction horizon (PH) ≥ 30 minutes, insulin and CHO information improves prediction accuracy of 10% and double the temporal gain during the 2 hours following the meal. During the night the 4 methods did not give statistically different results. When PH ≥ 45 minutes, the influence of CHO information on prediction is 5-fold that of insulin. Conclusions: In an open-loop setting, with PH ≥ 30 minutes, information on CHO and insulin improves short-term glucose prediction in the 2-hour time window following a meal, but not during the night. CHO information improves prediction significantly more than insulin.

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confidence: 99%

mentioning

confidence: 99%

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

Zecchin

Facchinetti

Sparacino

et al. 2016

J Diabetes Sci Technol

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“…The present study follows our previous work in glucose prediction, 25 in which online adaptive AR, ARX-and RNNbased models were developed and comparatively assessed with in silico data.…”

Section: Discussionmentioning

confidence: 99%

“…An extensive and detailed description of the models' algorithmic background and design methods is presented in our previous work. 25 A novel module was added to the ARX model in order to correct the model's output based on the estimated prediction error. The principle behind model output correction lies on the development of a submodel for the association of the prediction error of a specified PH to current glucose features and the use of this submodel during evaluation to modify the ARX model's output.…”

Section: Online Adaptive Autoregressive Models With Output Correctionmentioning

confidence: 99%

An Early Warning System for Hypoglycemic/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

Daskalaki

Nørgaard

Zueger

et al. 2013

J Diabetes Sci Technol

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Abbreviations: (ANN) artificial neural network, (AR) autoregressive, (ARMA) autoregressive moving average, (ARX) autoregressive with external input, (cARN) autoregressive with an output correction module/recurrent neural network, (cARX) autoregressive with an output correction module, (CC) correlation coefficient, (CGM) continuous glucose monitor, (CHO) carbohydrate, (EWS) early warning system, (HbA1c) glycosylated hemoglobin, (MDII) multiple daily insulin injection, (PH) prediction horizon, (RMSE) root mean square error, (RNN) recurrent neural network, (SAP) sensor-augmented pump, (T1DM) type 1 diabetes mellitus, (TL) time lag

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“…Unfortunately this is rarely the case and prediction models that are identifiable with only BG measurements should be preferred. This justifies the widespread use of black-box models, such as auto-regressive models [9], or neural networks [10][11][12]. These models, however, have the disadvantage that their parameters cannot be linked to physically observable quantities.…”

Section: Introductionmentioning

confidence: 99%

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

Bock

François

Gillet

2015

Computer Methods and Programs in Biomedicine

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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.

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Real-Time Adaptive Models for the Personalized Prediction of Glycemic Profile in Type 1 Diabetes Patients

Abstract: The ANN appears to be more appropriate for the prediction of glucose profile based on glucose and insulin data.

Cited by 67 publications

References 21 publications

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

How Much Is Short-Term Glucose Prediction in Type 1 Diabetes Improved by Adding Insulin Delivery and Meal Content Information to CGM Data? A Proof-of-Concept Study

An Early Warning System for Hypoglycemic/Hyperglycemic Events Based on Fusion of Adaptive Prediction Models

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

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