@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 = {<p>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.</p>
},
	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 {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 = {<p>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.</p>
},
	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}
}
