Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems

Hajizadeh, Iman; Rashid, Mudassir; Turksoy, Kamuran; Samadi, Sediqeh; Feng, Jun‐e; Sevil, Mert; Hobbs, Nicole; Lazaro, C. Burgos; Maloney, Zacharie; Littlejohn, Elizabeth; Çınar, Ali

doi:10.1177/1932296818789951

Cited by 48 publications

(25 citation statements)

References 47 publications

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“…The therapies we proposed typically maintain the patient in the healthy region even under variable conditions and patient behavior. Note, however, that the proposed therapies are open-loop (the drug schedule is computed only from the condition of the patient at the initial time), thus cannot compensate for unexpected behavior that can arise due to modeling simplifications (e.g., we do not consider how physical activity influences the blood glucose production and consumption [65,66]), measurement noise or bias. A step towards the real application of our methodology is a real-time closed-loop strategy; this is possible, since the typical time needed to compute an optimal solution on a standard laptop (i7-8550U CPU with 16GB RAM) is around 2 minutes.…”

Section: Discussionmentioning

confidence: 99%

Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon

et al. 2019

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The Glucose-Insulin-Glucagon nonlinear model accurately describes how the body responds to exogenously supplied insulin and glucagon in patients affected by Type I diabetes. Based on this model, we design infusion rates of either insulin (monotherapy) or insulin and glucagon (dual therapy) that can optimally maintain the blood glucose level within desired limits after consumption of a meal and prevent the onset of both hypoglycemia and hyperglycemia. This problem is formulated as a nonlinear optimal control problem, which we solve using the numerical optimal control package . Interestingly, in the case of monotherapy, we find the optimal solution is close to the standard method of insulin based glucose regulation, which is to assume a variable amount of insulin half an hour before each meal. We also find that the optimal dual therapy (that uses both insulin and glucagon) is better able to regulate glucose as compared to using insulin alone. We also propose an ad-hoc rule for both the dosage and the time of delivery of insulin and glucagon.

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Section: Discussionmentioning

confidence: 99%

Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon

et al. 2019

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“…We will also demonstrate how the software development experience can extend the research objectives while simultaneously enriching the education and training experiences of undergraduate and graduate students. In this case study, a REST API, a mobile application, and companion containerized backend system were developed to serve as the implementation of a detailed set of innovative algorithms related to multivariable AID systems [2][3][4]. Combining the engineering research with the requirements for software development and computational hardware resources was not trivial.…”

Section: Figure 1 Diagram Illustrating the Information Technologymentioning

confidence: 99%

“…This initial work was undertaken by researchers in the Chemical and Biological Engineering and Biomedical Engineering departments. They worked to develop hard science that became the foundation of this project and case-study [2][3][4]. The details of the insulin dosing algorithms are beyond the focus of this work and thus omitted in this paper, with the algorithm details reported elsewhere [2][3][4].…”

Section: Project Backgroundmentioning

confidence: 99%

“…They worked to develop hard science that became the foundation of this project and case-study [2][3][4]. The details of the insulin dosing algorithms are beyond the focus of this work and thus omitted in this paper, with the algorithm details reported elsewhere [2][3][4]. As the algorithms were developed and tested, it increasingly became clear that the algorithms needed to transition from research to a real-world product.…”

Section: Project Backgroundmentioning

confidence: 99%

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The Necessity of Interdisciplinary Software Development for Building Viable Research Platforms

Hajek

Rashid

Sevil

et al. 2020

Proceedings of the 21st Annual Conference on Information Technology Education

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Developing viable and robust software is an inescapable artifact of graduate research. The challenges lie in the complexities of developing, deploying, and securing software to support the research objectives. Combined with the transitive nature of students, the management of the software development and launch process is an arduous task. A standardized framework for developing and launching complex software is required. Within a university, individual departments do not typically possess the expertise, resources, software and infrastructure to translate research results to a viable product or tool. Extending upon the research of Hilton et al., [5] we designed a software development pipeline in an integrated multidisciplinary research context. The integrated and collaborative software pipeline formulated from the onset of the project streamlines the development phase and provides an iterative feedback and testing environment. This approach is applied to the development of automated insulin delivery systems, with the synergistic efforts of interdisciplinary teams yielding a mobile application and server software solutions, and a framework for the iterative advancement of the software capabilities into the future.

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“…Note that model (16) does not account for the meal for prediction purposes since this disturbance is assumed to be mostly covered by the premeal bolus. Moreover, the model prediction for every scenario is the same except for the disturbance realization; Equation (17) is the output equation at the i th scenario; Equations (18) and (19) ensure both insulin infusion and the difference between two consecutive insulin infusions along the control horizon to be in the intervals u u ] , respectively; Equations (20) and (21) are together a soft constraint over the output's lower bound;…”

Section: Multistage Model Predictive Controlmentioning

confidence: 99%

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

García-Tirado

Colmegna

Corbett

et al. 2019

J Diabetes Sci Technol

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Background: Maintaining glycemic equilibrium can be challenging for people living with type 1 diabetes (T1D) as many factors (eg, length, type, duration, insulin on board, stress, and training) will impact the metabolic changes triggered by physical activity potentially leading to both hypoglycemia and hyperglycemia. Therefore, and despite the noted health benefits, many individuals with T1D do not exercise as much as their healthy peers. While technology advances have improved glucose control during and immediately after exercise, it remains one of the key limitations of artificial pancreas (AP) systems, largely because stopping insulin at the onset of exercise may not be enough to prevent impending, exercise-induced hypoglycemia. Methods: A hybrid AP algorithm with subject-specific exercise behavior recognition and anticipatory action is designed to prevent hypoglycemic events during and after moderate-intensity exercise. Our approach relies on a number of key innovations, namely, an activity informed premeal bolus calculator, personalized exercise pattern recognition, and a multistage model predictive control (MS-MPC) strategy that can transition between reactive and anticipatory modes. This AP design was evaluated on 100 in silico subjects from the most up-to-date FDA-accepted UVA/Padova metabolic simulator, emulating an outpatient clinical trial setting. Results with a baseline controller, a regular MPC (rMPC), are also included for comparison purposes. Results: In silico experiments reveal that the proposed MS-MPC strategy markedly reduces the number of exercise-related hypoglycemic events (8 vs 68). Conclusion: An anticipatory mode for insulin administration of a monohormonal AP controller reduces the occurrence of hypoglycemia during moderate-intensity exercise.

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Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems

Abstract: The approach presented is able to identify reliable time-varying individualized glucose-insulin models.

Cited by 48 publications

References 47 publications

Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon

Optimal regulation of blood glucose level in Type I diabetes using insulin and glucagon

The Necessity of Interdisciplinary Software Development for Building Viable Research Platforms

In Silico Analysis of an Exercise-Safe Artificial Pancreas With Multistage Model Predictive Control and Insulin Safety System

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