@article {345, title = {Personalized Model Identification for Glucose Dynamics From Clinical Data With Incomplete Inputs}, journal = {IEEE Transactions on Biomedical Engineering}, volume = {72}, year = {2025}, month = {June}, pages = {2001-2012}, abstract = {

Objective: A common challenge in model identification with clinical data is incomplete and sometimes imprecise information. In this work, we provide a method to reconstruct the corrupted input data in a clinical dataset and, jointly identify the person-specific parameters of a metabolic model describing meal-insulin-glucose-dynamics for people with type 1 diabetes (T1D). Method: The proposed method is an algorithm that iterates between nonlinear least-squares and mixed-integer quadratic programming to optimize model parameters in conjunction with sparse corrections to the input data. In order to handle long stretches of data, the optimization problem is designed to be i) computationally tractable, and ii) robust against the potential presence of significant inaccuracies corrupting a data portion. Moreover, since the pattern of the inaccuracies is specific to each person, we propose a personalized hyperparameter tuning approach. The method is applied on clinical data from 13 people with T1D. Identified model performance is compared to the performance of model identified with standard least squares (LS) method. Results: Compared to LS, identifying corrections in conjunction with model parameters on training data lead to an improvement in the model prediction capabilities on unseen data with an average 2.2\% improvement in MARD for two-hour prediction horizon (p-value = 0.0006). Conclusions: The proposed method is effective in model identification for clinical data with unknown inaccuracies in the inputs. Significance: Personalized models with high accuracy can inform treatment decisions and lead to better glucose control outcomes in people with T1D.

}, keywords = {Adaptation models, Data models, Diabetes, Glucose, Incomplete data, insulin, Mathematical models, mixed-integer optimization, model identification, Postal services, Predictive models, Standards, Training data, type 1 diabetes}, issn = {1558-2531}, doi = {10.1109/TBME.2025.3530711}, author = {Ozaslan, Basak and Aiello, Eleonora M. and Fushimi, Emilia and Doyle, Francis J. and Dassau, Eyal} } @article {344, title = {Robust dual-hormone controller for full closed-loop glucose regulation in people with type 1 diabetes: An in silico study}, journal = {Biocybernetics and Biomedical Engineering}, volume = {45}, year = {2025}, pages = {329-339}, abstract = {

Current advanced methods for glucose control in people with type 1 diabetes (T1D), often referred to as artificial pancreas (AP) or automated insulin delivery (AID) systems, rely on the administration of a single hormone (insulin) to regulate blood glucose (BG). In general, these systems depend on patient-specific information usually obtained from the conventional insulin therapy to account for inter-patient variability. On the other hand, dual-hormone (DH) systems that use insulin and its counterregulatory hormone, glucagon, have the potential of further improving BG control. However, DH systems are still under development or in earlier testing stages. Since glucagon is not used in the traditional therapy for T1D, the sensitivity of each individual to this hormone is typically unknown. Here, a DH controller based on robust control is proposed. The controller in charge of glucagon dosing, based on H$\infty$ optimal control, does not require any individualization, thus overcoming one of the challenges faced by DH approaches. The strategy is evaluated in silico and compared to previous works involving a personalized glucagon controller and its single-hormone counterpart. Results suggest that the robust control strategy allows satisfactory glucose outcomes without the need for individualization.

}, keywords = {Dual-hormone control, Robust control, Switched control, type 1 diabetes}, issn = {0208-5216}, doi = {https://doi.org/10.1016/j.bbe.2025.05.004}, url = {https://www.sciencedirect.com/science/article/pii/S0208521625000324}, author = {Fushimi, Emilia and Bianchi, Fernando Daniel and De Battista, Hern{\'a}n and Garelli, Fabricio} } @article {291, title = {Control-Oriented Model with Intra-Patient Variations for an Artificial Pancreas}, journal = {IEEE Journal of Biomedical and Health Informatics}, year = {2020}, pages = {1-1}, abstract = {

In this work, a low-order model designed for glucose regulation in T1DM is obtained from the UVA/Padova metabolic simulator. It captures not only the nonlinear behavior of the glucose-insulin system, but also intra-patient variations related to daily insulin sensitivity (${S_{I}}$) changes. To overcome the large inter-subject variability, the model can also be personalized based on a priori patient information. The structure is amenable for linear parameter varying (LPV) controller design, and represents the dynamics from the subcutaneous insulin input to the subcutaneous glucose output. The efficacy of this model is evaluated in comparison with a previous control-oriented model which in turn is an improvement of previous models. Both models are compared in terms of their open- and closed-loop differences with respect to the UVA/Padova model. The open-loop analysis is computed through the root mean square error (RMSE) between the glucose deviation predicted by the models and the UVA/Padova simulator. In closed-loop, the ν-gap metric is used to measure the distance in terms of closed-loop performance between the two models. The results are promising and some illustrative simulations are presented.

}, keywords = {Artificial pancreas, Control-oriented model, Intra-patient variations, LPV model, type 1 diabetes}, issn = {2168-2208}, doi = {10.1109/JBHI.2020.2969389}, author = {Moscoso-V{\'a}squez, Marcela and Colmegna, Patricio and Rosales, Nicol{\'a}s and Garelli, Fabricio and S{\'a}nchez-Pe{\~n}a, Ricardo} } @article {50, title = {Safety Auxiliary Feedback Element for the Artificial Pancreas in Type 1 Diabetes}, journal = {Biomedical Engineering, IEEE Transactions on}, volume = {60}, year = {2013}, month = {Aug}, pages = {2113-2122}, abstract = {

The artificial pancreas aims at the automatic delivery of insulin for glycemic control in patients with type 1 diabetes, i.e., closed-loop glucose control. One of the challenges of the artificial pancreas is to avoid controller overreaction leading to hypoglycemia, especially in the late postprandial period. In this study, an original proposal based on sliding mode reference conditioning ideas is presented as a way to reduce hypoglycemia events induced by a closed-loop glucose controller. The method is inspired in the intuitive advantages of two-step constrained control algorithms. It acts on the glucose reference sent to the main controller shaping it so as to avoid violating given constraints on the insulin-on-board. Some distinctive features of the proposed strategy are that 1) it provides a safety layer which can be adjusted according to medical criteria; 2) it can be added to closed-loop controllers of any nature; 3) it is robust against sensor failures and overestimated prandial insulin doses; and 4) it can handle nonlinear models. The method is evaluated in silico with the ten adult patients available in the FDA-accepted UVA simulator.

}, keywords = {Absorption, Artificial, artificial organs, Artificial pancreas, Biological, Blood Glucose Self-Monitoring, closed loop systems, closed-loop glucose control, Computer Simulation, Computer-Assisted, Diabetes, Diabetes Mellitus, diseases, drug delivery systems, Drug Therapy, drugs, Equipment Safety, FDA-accepted UVA simulator, feedback, glucose control, glycemic control, Humans, hypoglycemia, insulin, insulin-on-board, medical control systems, Models, Pancreas, Physiological, postprandial period, Proposals, reference conditioning, Robustness, safety, safety auxiliary feedback element, sliding mode (SM), sliding mode reference conditioning, Sugar, two-step constrained control algorithms, Type 1, type 1 diabetes}, issn = {0018-9294}, doi = {10.1109/TBME.2013.2247602}, url = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6463437}, author = {Revert, Ana and Garelli, Fabricio and Pic{\'o}, Jes{\'u}s and De Battista, Hern{\'a}n and Rossetti, Paolo and Veh{\'\i}, Josep and Bondia, Jorge} }