Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

Hoyos, Juan; Villa-Tamayo, María F.; Builes, Carlos; Rincón, Alex Ramírez; Godoy, José Luis; García-Tirado, José; Rivadeneira, Pablo S.

doi:10.1109/access.2021.3076405

Cited by 9 publications

(6 citation statements)

References 90 publications

(98 reference statements)

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“…The current model structures could then be modified to include more realistic time dependent parameters (e.g., seasonality terms) or consider using a structure with impulsive differential equations. In addition, it would be interesting to evaluate the predictive capabilities of linear versions of these models, similar to those reviewed in [ 73 ], and investigate the structural and practical identifiability [ 73 , 74 ]. Despite these shortfalls, our modelling represents an interesting step in developing data-driven dynamic models for diabetes, to improve the prediction of blood glucose concentrations using deterministic compartmental models.…”

Section: Discussionmentioning

confidence: 99%

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Ali

Daneshkhah

Boutayeb

et al. 2022

IJERPH

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Type 1 diabetes requires treatment with insulin injections and monitoring glucose levels in affected individuals. We explored the utility of two mathematical models in predicting glucose concentration levels in type 1 diabetic mice and determined disease pathways. We adapted two mathematical models, one with β-cells and the other with no β-cell component to determine their capability in predicting glucose concentration and determine type 1 diabetes pathways using published glucose concentration data for four groups of experimental mice. The groups of mice were numbered Mice Group 1–4, depending on the diabetes severity of each group, with severity increasing from group 1–4. A Markov Chain Monte Carlo method based on a Bayesian framework was used to fit the model to determine the best model structure. Akaike information criteria (AIC) and Bayesian information criteria (BIC) approaches were used to assess the best model structure for type 1 diabetes. In fitting the model with no β-cells to glucose level data, we varied insulin absorption rate and insulin clearance rate. However, the model with β-cells required more parameters to match the data and we fitted the β-cell glucose tolerance factor, whole body insulin clearance rate, glucose production rate, and glucose clearance rate. Fitting the models to the blood glucose concentration level gave the least difference in AIC of 1.2, and a difference in BIC of 0.12 for Mice Group 4. The estimated AIC and BIC values were highest for Mice Group 1 than all other mice groups. The models gave substantial differences in AIC and BIC values for Mice Groups 1–3 ranging from 2.10 to 4.05. Our results suggest that the model without β-cells provides a more suitable structure for modelling type 1 diabetes and predicting blood glucose concentration for hypoglycaemic episodes.

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Section: Discussionmentioning

confidence: 99%

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Ali

Daneshkhah

Boutayeb

et al. 2022

IJERPH

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“…Here, the impulsive discretization of a minimal physiological model based on five compartments to represent glucose dynamics, insulin absorption and action, and meal absorption dynamics is used. It is a linear model that satisfy global structural identifiability ( 35 ). The state-space representation of the model is given by…”

Section: Methodsmentioning

confidence: 99%

“…These values are obtained with model (4) and the input u OL corresponding to the open-loop insulin dose that should be administered to the subject, i.e., u OL = u basal for fasting periods, and u OL = CHO/CR when a meal is announced. CHO corresponds to the carbohydrates ingested, and CR is the insulin-to-CHO ratio ( 35 ).…”

Section: Methodsmentioning

confidence: 99%

Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability

et al. 2022

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The aim of control strategies for artificial pancreas systems is to calculate the insulin doses required by a subject with type 1 diabetes to regulate blood glucose levels by reducing hyperglycemia and avoiding the induction of hypoglycemia. Several control formulations developed for this end involve a safety constraint given by the insulin on board (IOB) estimation. This constraint has the purpose of reducing hypoglycemic episodes caused by insulin stacking. However, intrapatient variability constantly changes the patient’s response to insulin, and thus, an adaptive method is required to restrict the control action according to the current situation of the subject. In this work, the control action computed by an impulsive model predictive controller is modulated with a safety layer to satisfy an adaptive IOB constraint. This constraint is established with two main steps. First, upper and lower IOB bounds are generated with an interval model that accounts for parameter uncertainty, and thus, define the possible system responses. Second, the constraint is selected according to the current value of glycemia, an estimation of the plant-model mismatch, and their corresponding first and second time derivatives to anticipate the changes of both glucose levels and physiological variations. With this strategy satisfactory results were obtained in an adult cohort where random circadian variability and sensor noise were considered. A 92% time in normoglycemia was obtained, representing an increase of time in range compared to previous MPC strategies, and a reduction of time in hypoglycemia to 0% was achieved without dangerously increasing the time in hyperglycemia.

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“…A primary challenge in managing glucose levels for individuals with type 1 diabetes is identifying a control-oriented model capable of precisely predicting glycemic behaviour. This paper examines such models' structural identifiability and observability properties, highlighting that only a few are globally identifiable and observable concurrently [9]. Diabetes Mellitus is increasingly prevalent globally, posing various challenges for public health policies.…”

Section: Literature Surveymentioning

confidence: 99%

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

N. Laxmi

2023

IJRITCC

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This paper addresses the problem of predicting insulin dosage in diabetes patients using the PSO-LSTM, COA-LSTM, and LOA-LSTM algorithms. Accurate insulin dosage prediction is crucial in effectively managing Diabetes and maintaining blood glucose levels within the desired range. The study proposes a novel approach that combines particle swarm optimization (PSO) with the long short-term memory (LSTM) model. PSO is used to optimize the LSTM's parameters, enhancing its prediction capabilities specifically for insulin dosage. Additionally, two other techniques, COA-LSTM and LOA-LSTM, are introduced for comparison purposes. The algorithms utilize a dataset comprising relevant features such as past insulin dosages, blood glucose levels, carbohydrate intake, and physical activity. These features are fed into the PSO-LSTM, COA-LSTM, and LOA-LSTM models to predict the appropriate insulin dosage for future time points. The results demonstrate the effectiveness of the proposed PSO-LSTM algorithm in accurately predicting insulin dosage, surpassing the performance of COA-LSTM and LOA-LSTM. The PSO-LSTM model achieves a high level of accuracy, aiding in personalized and precise insulin administration for diabetes patients. By leveraging the power of PSO optimization and LSTM modeling, this research improves the accuracy and reliability of insulin dosage prediction. The findings highlight the potential of the PSO-LSTM algorithm as a valuable tool for healthcare professionals in optimizing diabetes management and enhancing patient outcomes.

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Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

Cited by 9 publications

References 90 publications

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Examining Type 1 Diabetes Mathematical Models Using Experimental Data

Interval Safety Layer Coupled With an Impulsive MPC for Artificial Pancreas to Handle Intrapatient Variability

Hybrid Deep Learning Algorithm for Insulin Dosage Prediction Using Blockchain and IOT

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