Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting

D’Antoni, Federico; Merone, Mario; Piemonte, Vincenzo; Iannello, Giulio; Soda, Paolo

doi:10.1016/j.knosys.2020.106134

Cited by 20 publications

(25 citation statements)

References 16 publications

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“…At present, the continuous BG trend prediction systems with high and low BG alarm lines to generate timely warnings always have different degrees of deviation [ 5 , 6 ]. The reason is that the injected insulin takes a particular time to reduce the BG levels.…”

Section: Introductionmentioning

confidence: 99%

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Zhang

Gao

2022

Journal of Healthcare Engineering

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Continuous noninvasive blood glucose monitoring and estimation management by using photoplethysmography (PPG) technology always have a series of problems, such as substantial time variability, inaccuracy, and complex nonlinearity. This paper proposes a blood glucose (BG) prediction model for more precise prediction based on BG series decomposition by complete aggregation empirical mode decomposition based on adaptive white noise (CEEMDAN) and the gated recurrent unit (GRU) that is optimized by improved bacterial foraging optimization (IBFO). Hierarchical clustering technology recombines the decomposed BG series according to their sample entropy and the correlations with the original BG trends. Dynamic BG trends are regressed separately for each recombined BG series by the GRU model to realize the more precise estimations, which are optimized by IBFO for its structure and superparameters. Through experiments, the optimized and basic LSTM, RNN, and support vector regression (SVR) are compared to evaluate the performance of the proposed model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of the 15-min IBFO-GRU prediction is improved on average by about 13.1% and 18.4%, respectively, compared with those of the RNN and LSTM optimized by IBFO. Meanwhile, the proposed model improved the Clarke error grid results by about 2.6% and 5.0% compared with those of the IBFO-LSTM and IBFO-RNN in 30-min prediction and by 4.1% and 6.6% in 15-min ahead forecast, respectively. The evaluation outcomes of our proposed CEEMDAN-IBFO-GRU model have high accuracy and adaptability and can effectively provide early intervention control of the occurrence of hyperglycemic complications.

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Section: Introductionmentioning

confidence: 99%

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Zhang

Gao

2022

Journal of Healthcare Engineering

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“…Tennessee Eastman Process. The prediction efficiency of the proposed KrLVR model is demonstrated by modeling the Tennessee Eastman process, which is a wellknown benchmark representation of a real industrial process for assessing process modeling, monitoring, and control (5) compressor recycle valve XMEAS (5) recycle flow XMEAS( 16) stripper pressure XMV (6) purge valve XMEAS (6) reactor feed rate XMEAS (17) stripper under flow XMV (7) separator pot liquid flow XMEAS (7) reactor pressure XMEAS(18) stripper temperature XMV (8) stripper liquid product flow XMEAS (8) reactor level XMEAS (19) stripper steam flow XMV (9) stripper steam XMEAS (9) reactor temperature XMEAS (20) compressor work XMV (10) reactor cooling water flow XMEAS(10) purge rate XMEAS (21) reactor cooling water outlet XMV (11) condenser cooling water flow XMEAS (11) separator temperature XMEAS (22) condenser cooling water outlet temp 2 and 3, respectively. 14,29 A total number of 960 samples from IDV(1) were collected; a typical disturbance where a step change is introduced in the variable of A/C ratio while keeping the B composition constant and the first 200 samples are utilized for developing models.…”

Section: Case Studiesmentioning

confidence: 99%

Kernel-Regularized Latent-Variable Regression Models for Dynamic Processes

Sun

Çınar

Xia

et al. 2022

Ind. Eng. Chem. Res.

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Establishing relations between variables and realtime prediction of quality variables or other key indicators are critical for dynamic processes including industrial and biological processes. In this study, a novel multivariate statistical modeling method named "kernel-regularized latent variable regression (KrLVR) approach" is proposed for capturing the dynamics of a process by building KrLVR models with process and quality data. First, a regularization term based on a kernel matrix is incorporated into the objective of the latent variable regression model. Consequently, the proposed KrLVR method has the ability to overcome potential ill-conditioning resulting from collinearity in process data and has a stronger prediction power. Besides, the inner model is consistent with the outer model, which enables the proposed method to predict quality data with fewer latent variables. Second, the prior knowledge of dynamic processes such as exponential stability and smoothness can be integrated into the modeling process by using an appropriate kernel matrix. In addition, to meet the requirement of exponential stability of the model, the weights of the model should decay exponentially, that is, coincident with determining the number of historical observations for data augmentation (identification of the model structure). Therefore, the problem of tuning model complexity is eluded, and it becomes finding appropriate hyper-parameters of the kernel matrix. Moreover, the empirical Bayesian method is utilized for estimating hyper-parameters of the kernel matrix from augmented process and quality data. Three case studies illustrate the performance of the proposed KrLVR method by comparing with several other relevant methods.

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“…ARTiDe presented in [9], which is a model for forecasting blood glucose levels based on a jump neural network JNN to handle both the linear and nonlinear components of inputs data. The model includes temporal delays for the input signals with auto-regressive feedbacks.…”

Section: Review Of the State-of-the-artmentioning

confidence: 99%

“…In addition, activations of hidden or visible units can be conditionally dependent on visible or hidden units; The conditional probability of h depending on v is defined as in Equations ( 6)- (9).…”

Section: Restricted Boltzmann Machines (Rbm)mentioning

confidence: 99%

IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes

et al. 2021

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Diabetes is a chronic disease that can affect human health negatively when the glucose levels in the blood are elevated over the creatin range called hyperglycemia. The current devices for continuous glucose monitoring (CGM) supervise the glucose level in the blood and alert user to the type-1 Diabetes class once a certain critical level is surpassed. This can lead the body of the patient to work at critical levels until the medicine is taken in order to reduce the glucose level, consequently increasing the risk of causing considerable health damages in case of the intake is delayed. To overcome the latter, a new approach based on cutting-edge software and hardware technologies is proposed in this paper. Specifically, an artificial intelligence deep learning (DL) model is proposed to predict glucose levels in 30 min horizons. Moreover, Cloud computing and IoT technologies are considered to implement the prediction model and combine it with the existing wearable CGM model to provide the patients with the prediction of future glucose levels. Among the many DL methods in the state-of-the-art (SoTA) have been considered a cascaded RNN-RBM DL model based on both recurrent neural networks (RNNs) and restricted Boltzmann machines (RBM) due to their superior properties regarding improved prediction accuracy. From the conducted experimental results, it has been shown that the proposed Cloud&DL-based wearable approach achieves an average accuracy value of 15.589 in terms of RMSE, then outperforms similar existing blood glucose prediction methods in the SoTA.

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Auto-Regressive Time Delayed jump neural network for blood glucose levels forecasting

Cited by 20 publications

References 16 publications

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Optimization and Evaluation of an Intelligent Short-Term Blood Glucose Prediction Model Based on Noninvasive Monitoring and Deep Learning Techniques

Kernel-Regularized Latent-Variable Regression Models for Dynamic Processes

IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes

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