Assessment of Blood Glucose Predictors: The Prediction-Error Grid Analysis

Sampath, Sivananthan; Naumova, Valeriya; Man, Chiara Dalla; Facchinetti, Andrea; Renard, Éric; Cobelli, Claudio; Pereverzyev, Sergiy

doi:10.1089/dia.2011.0033

Cited by 30 publications

(39 citation statements)

References 17 publications

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“…Clarke error-grid analysis (CEGA) [30][31][32] is a popular method to evaluate the BG prediction performance. In brief, a larger percentage in Zone A means better prediction performance.…”

Section: The Relative Error Analysis Is Defined As Followsmentioning

confidence: 99%

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

Wang

Wu²,

Mo³

2013

Diabetes Technology & Therapeutics

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Background: Blood glucose (BG) prediction plays a very important role in daily BG management of patients with diabetes mellitus. Several algorithms, such as autoregressive (AR) models and artificial neural networks, have been proposed for BG prediction. However, every algorithm has its own subject range (i.e., one algorithm might work well for one diabetes patient but poorly for another patient). Even for one individual patient, this algorithm might perform well during the preprandial period but poorly during the postprandial period. Materials and Methods: A novel framework was proposed to combine several BG prediction algorithms. The main idea of the novel framework is that an adaptive weight is given to each algorithm where one algorithm's weight is inversely proportional to the sum of the squared prediction errors. In general, this framework can be applied to combine any BG prediction algorithms. Results: As an example, the proposed framework was used to combine an AR model, extreme learning machine, and support vector regression. The new algorithm was compared with these three prediction algorithms on the continuous glucose monitoring system (CGMS) readings of 10 type 1 diabetes mellitus patients; the CGMS readings of each patient included 860 CGMS data points. For each patient, the algorithms were evaluated in terms of root-mean-square error, relative error, Clarke error-grid analysis, and J index. Of the 40 evaluations, the new adaptive-weighted algorithm achieved the best prediction performance in 37 (92.5%). Conclusions: Thus, we conclude that the adaptive-weighted-average framework proposed in this study can give satisfactory predictions and should be used in BG prediction. The new algorithm has great robustness with respect to variations in data characteristics, patients, and prediction horizons. At the same time, it is universal.

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“…Clarke error-grid analysis (CEGA) [30][31][32] is a popular method to evaluate the BG prediction performance. In brief, a larger percentage in Zone A means better prediction performance.…”

Section: The Relative Error Analysis Is Defined As Followsmentioning

confidence: 99%

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

Wang

Wu²,

Mo³

2013

Diabetes Technology & Therapeutics

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“…The main aim of this section is to discuss the performance of the method (24) equipped with the adaptive parameter choice rule (31) in predicting the blood glucose (BG) evolution.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…These simulated measurements have been corrupted by random white noise with the standard deviation δ of 6 (mg/dL). We perform our illustrative tests with data of the same 10 virtual subjects that have been considered in [25], [31].…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…To quantify the clinical accuracy of the considered predictors, we use the Prediction Error-Grid Analysis (PRED-EGA) [31], which has been designed especially for the blood glucose predictors. This assessment methodology records reference glucose estimates paired with the estimates predicted for the same moments.…”

Section: Numerical Experimentsmentioning

confidence: 99%

“…We would like to stress that the assessment has been done with respect to the references given as simulated noise-free BG-readings. Table 1 demonstrates the performance assessment matrix given by the PRED-EGA for 15 (min) ahead glucose predictions by the linear extrapolation predictors, where the derivative is estimated by means of (35), (36) with the parameter chosen in accordance with (31), operating on simulated noisy data with SH = 30 (min). Table 1 The performance assessment matrix given by the PRED-EGA for the linear extrapolation predictors, where the derivative is found by (35), (36) with a truncation level chosen by the balancing principle (31), operating on simulated noisy data with P H = 15 (min) and SH = 30 (min) We perform the comparison of the constructed predictors with the predictors considered in [13, ?…”

Section: Numerical Experimentsmentioning

confidence: 99%

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Filtered Legendre expansion method for numerical differentiation at the boundary point with application to blood glucose predictions

Mhaskar

Naumova

Pereverzyev

2013

Applied Mathematics and Computation

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Let f : [−1, 1] → R be continuously differentiable. We consider the question of approximating f ′ (1) from given data of the form (t j , f (t j )) M j=1 where the points t j are in the interval [−1, 1]. It is well known that the question is ill-posed, and there is very little literature on the subject known to us. We consider a summability operator using Legendre expansions, together with high order quadrature formulas based on the points t j 's to achieve the approximation. We also estimate the effect of noise on our approximation. The error estimates, both with or without noise, improve upon those in the existing literature, and appear to be unimprovable. The results are applied to the problem of short term prediction of blood glucose concentration, yielding better results than other comparable methods.

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Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

Diouri

Cigler

Vettoretti

et al. 2021

Diabetes Metabolism Res

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The main objective of diabetes control is to correct hyperglycaemia while avoiding hypoglycaemia, especially in insulin-treated patients. Fear of hypoglycaemia is a hurdle to effective correction of hyperglycaemia because it promotes under-dosing of insulin. Strategies to minimise hypoglycaemia include education and training for improved hypoglycaemia awareness and the development of technologies to allow their early detection and thus minimise their occurrence. Patients with impaired hypoglycaemia awareness would benefit the most from these technologies. The purpose of this systematic review is to review currently available or in-development technologies that support detection of hypoglycaemia or hypoglycaemia risk, and identify gaps in the research. Nanomaterial use in sensors is a promising strategy to increase the accuracy of continuous glucose monitoring devices for low glucose values. Hypoglycaemia is associated with changes on vital signs, so electrocardiogram and encephalogram could also be used to detect hypoglycaemia. Accuracy improvements through multivariable measures can make already marketed galvanic skin response devices a good noninvasive alternative. Breath volatile organic compounds can be detected by dogs and devices and alert patients at hypoglycaemia onset, while near-infrared spectroscopy can also be used as a hypoglycaemia alarms. Finally, one of the main directions of research are deep learning algorithms to analyse continuous glucose monitoring data and provide earlier and more accurate prediction of hypoglycaemia. Current developments for early identification of hypoglycaemia risk combine improvements of available 'needle-type' enzymatic glucose sensors and noninvasive alternatives. Patient usability will be essential to demonstrate to allow their implementation for daily use in diabetes management.

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Assessment of Blood Glucose Predictors: The Prediction-Error Grid Analysis

Cited by 30 publications

References 17 publications

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

A Novel Adaptive-Weighted-Average Framework for Blood Glucose Prediction

Filtered Legendre expansion method for numerical differentiation at the boundary point with application to blood glucose predictions

Hypoglycaemia detection and prediction techniques: A systematic review on the latest developments

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