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Your search keyword '"Aradottir, Tinna"' showing total 22 results
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22 results on '"Aradottir, Tinna"'

1. Identification of PK-PD Insulin Models using Experimental GIR Data

Author

Freil, Kirstine Sylvest, Fritzen, Liv Olivia, Boiroux, Dimitri, Aradottir, Tinna B., and Jørgensen, John Bagterp

Subjects
Quantitative Biology - Quantitative Methods
Abstract

We present a method to estimate parameters in pharmacokinetic (PK) and pharmacodynamic (PD) models for glucose insulin dynamics in humans. The method combines 1) experimental glucose infusion rate (GIR) data from glucose clamp studies and 2) a PK-PD model to estimate parameters such that the model fits the data. Assuming that the glucose clamp is perfect, we do not need to know the details of the controller in the clamp, and the GIR can be computed directly from the PK-PD model. To illustrate the procedure, we use the glucoregulatory model developed by Hovorka and modify it to have a smooth non-negative endogeneous glucose production (EGP) term. We estimate PK-PD parameters for rapid-acting insulin analogs (Fiasp and NovoRapid). We use these PK-PD parameters to illustrate GIR for insulin analogs with 30% and 50% faster absorption time than currently available rapid-acting insulin analogs. We discuss the role of system identification using GIR data from glucose clamp studies and how such identified models can be used in automated insulin dosing (AID) systems with ultra rapid-acting insulin., Comment: Submitted to be presented at a conference, 6 pages, 10 figures, 1 table

Published
2024

2. Estimating a Personalized Basal Insulin Dose from Short-Term Closed-Loop Data in Type 2 Diabetes

Author

Engell, Sarah Ellinor, Aradóttir, Tinna Björk, Ritschel, Tobias K. S., Bengtsson, Henrik, and Jørgensen, John Bagterp

Subjects
Electrical Engineering and Systems Science - Systems and Control
Abstract

In type 2 diabetes (T2D) treatment, finding a safe and effective basal insulin dose is a challenge. The dose-response is highly individual and to ensure safety, people with T2D titrate by slowly increasing the daily insulin dose to meet treatment targets. This titration can take months. To ease and accelerate the process, we use short-term artificial pancreas (AP) treatment tailored for initial titration and apply it as a diagnostic tool. Specifically, we present a method to automatically estimate a personalized daily dose of basal insulin from closed-loop data collected with an AP. Based on AP-data from a stochastic simulation model, we employ the continuous-discrete extended Kalman filter and a maximum likelihood approach to estimate parameters in a simple dose-response model for 100 virtual people. With the identified model, we compute a daily dose of basal insulin to meet treatment targets for each individual. We test the personalized dose and evaluate the treatment outcomes against clinical reference values. In the tested simulation setup, the proposed method is feasible. However, more extensive tests will reveal whether it can be deemed safe for clinical implementation., Comment: 6 pages, 4 figures, 1 table. Accepted for publication in Proceedings of the 2022 61st IEEE Conference on Decision and Control (CDC)

Published
2022

14. A New Stochastic Approach for Modeling Glycemic Disturbances in Type 2 Diabetes

Author

Clausen, Henrik, Knudsen, Torben, Al Ahdab, Mohamad, Aradottir, Tinna, Schmidt, Signe, Nørgaard, Kirsten, Leth, John, Clausen, Henrik, Knudsen, Torben, Al Ahdab, Mohamad, Aradottir, Tinna, Schmidt, Signe, Nørgaard, Kirsten, and Leth, John

Abstract

OBJECTIVE: To improve insulin treatment in type 2 diabetes (T2D) using model-based control techniques, the underlying model needs to be individualized to each patient. Due to the impact of unknown meals, exercise and other factors on the blood glucose, it is difficult to utilize available data from continuous glucose monitors (CGMs) for model fitting and parameter estimation purposes.METHODS: To overcome this problem, we propose a novel method for modeling the glycemic disturbances as a stochastic process. To differentiate meals from other glycemic disturbances, we model the meal intake as a separate stochastic process while encompassing all other disturbances in another stochastic process. Using particle filtering, we validate the model on simulations as well as on clinical data.RESULTS: Based on simulated CGM data, the residuals generated by the particle filter are white, indicating a good model fit. For the clinical data, we use parameter values estimated based on fasting glucose data. The residuals obtained from clinical CGM data contain correlations up to lag 5.CONCLUSION: The proposed model is shown to adequately describe the meal-induced glucose fluctuations in simulated CGM data while validations on clinical CGM data show promising results as well.SIGNIFICANCE: The proposed model may lay the grounds for new ways of utilizing available CGM data, including CGM-based parameter estimation and stochastic optimal control.

Published
2021

15. A Model-free Approach to Automatic Dose Guidance in Long Acting Insulin Treatment of Type 2 Diabetes

Author

Krishnamoorthy, Dinesh, Boiroux, Dimitri, Aradottir, Tinna Bjork, Engell, Sarah Ellinor, Jørgensen, John Bagterp, Krishnamoorthy, Dinesh, Boiroux, Dimitri, Aradottir, Tinna Bjork, Engell, Sarah Ellinor, and Jørgensen, John Bagterp

Abstract

This paper presents a model-free insulin titration algorithm for patients with type 2 diabetes that automatically finds and maintains the optimal insulin dosage in order to maintain the blood glucose concentration at desired levels. The proposed method is based on recursive least square based extremum seeking control. Since the proposed method does not require a detailed model, it can be applied on a wide population of patients without the need to identify and adapt models to the patient data. We demonstrate the effectiveness of the proposed method using in silico simulations, which are benchmarked against two standard-of-care approaches. We also show that the proposed method can handle intra-patient metabolic variations and non-adherence to the treatment regimen. Finally, using a population of 50 virtual patients, we show that the proposed method is able to handle inter-patient variations.

Published
2021

20. Correlation in Dose-Response to Rapid- and Long-Acting Insulin for People with Type 1 Diabetes.

Author

Engell SE, Aradottir TB, Bengtsson H, and Jorgensen JB

Subjects
Algorithms, Humans, Insulin, Insulin, Long-Acting, Diabetes Mellitus, Type 1 drug therapy, Insulin Resistance
Abstract

In diabetes, it can become necessary to switch between pump- and pen-based insulin treatment. This switch involves a translation between rapid- and long-acting insulin analogues. In standard-of-care translation algorithms, a unit-to-unit conversion is applied. However, this simplification may not fit all individuals. In this paper, we investigate the correlation between dose-response to rapid- and long-acting insulin in the same individual, and compare the correlation across individuals. As a measure of dose-response, we estimate the insulin sensitivity in clinical data from 25 subjects with type 1 diabetes. For parameter estimation, we use maximum likelihood with a continuous-discrete extended Kalman filter and Bergman's minimal model. The results show a weak correlation between insulin sensitivity to rapid- and long-acting insulin across individuals. On this sparse data set, the analysis suggests that the standardized unit-to-unit translation between insulin analogues may not benefit all subjects.

Published
2022
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21. Modelling of fasting glucose-insulin dynamics from sparse data .

Author

Aradottir TB, Boiroux D, Bengtsson H, and Poulsen NK

Subjects
Fasting, Humans, Blood Glucose analysis, Diabetes Mellitus, Type 2 blood, Insulin blood, Models, Biological
Abstract

With the fast growth of diabetes prevalence, the disease is now considered an epidemic. Diabetes is characterized by elevated glucose levels, that may be treated with insulin. Tight control of glucose is essential for prevention of complications and patients' well-being. In this paper we model the fasting glucose-insulin dynamics in type 2 diabetes, aiming at controlling the glucose level. Relevant clinical data are typically sparse and have a sampling period much greater than the fast dynamics in the glucose-insulin dynamics in humans. We adapt a physiological model such that important slow non-linear dynamics are identifiable and test the resulting model on deterministic simulated data and sparse, slow sampled clinical data.

Published
2018
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22. A deep learning approach to adherence detection for type 2 diabetics.

Author

Mohebbi A, Aradottir TB, Johansen AR, Bengtsson H, Fraccaro M, and Morup M

Subjects
Algorithms, Humans, Machine Learning, Neural Networks, Computer, Pilot Projects, Diabetes Mellitus, Type 2
Abstract

Diabetes has become one of the biggest health problems in the world. In this context, adherence to insulin treatment is essential in order to avoid life-threatening complications. In this pilot study, a novel adherence detection algorithm using Deep Learning (DL) approaches was developed for type 2 diabetes (T2D) patients, based on simulated Continuous Glucose Monitoring (CGM) signals. A large and diverse amount of CGM signals were simulated for T2D patients using a T2D adapted version of the Medtronic Virtual Patient (MVP) model for T1D. By using these signals, different classification algorithms were compared using a comprehensive grid search. We contrast a standard logistic regression baseline to Multi- Layer Perceptrons (MLPs) and Convolutional Neural Networks (CNNs). The best classification performance with an average accuracy of 77:5% was achieved with CNN. Hence, this indicates the potential of DL, when considering adherence detection systems for T2D patients.

Published
2017
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