Adaptive personalized multivariable artificial pancreas using plasma insulin estimates

Hajizadeh, Iman; Rashid, Mudassir; Samadi, Sediqeh; Sevil, Mert; Hobbs, Nicole; Brandt, Rachel; Çınar, Ali

doi:10.1016/j.jprocont.2019.05.003

Cited by 43 publications

(34 citation statements)

References 58 publications

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“…Many version of MPCs have been implemented in AP systems. They can be classified as [13]: unconstrained MPC [14], multiple-model adaptive MPC [15], zone MPC [16], set-point-based enhanced MPC (eMPC) [17], adaptive MPC [18,19], bi-hormonal MPC [20] and policy-based stochastic MPC [21,22]. These strategies were tested under in-silico simulators (UVA/Padova [23], Cambridge [24], Medtronic Virtual Patient [25]) and also, under in-vivo clinical trials.…”

Section: Introductionmentioning

confidence: 99%

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Abuin

Rivadeneira

Ferramosca

et al. 2020

Journal of Process Control

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Section: Introductionmentioning

confidence: 99%

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Abuin

Rivadeneira

Ferramosca

et al. 2020

Journal of Process Control

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“…Others such as Turksoy et al, 19 Hajizadeh et al, 32 and the algorithm presented in the current paper use exercise as a continuous input. The Cinar group 18,32 has taken a different approach with their multivariable MPC algorithm that adapts the model at every time point whereby exercise is one of many variables input into the model. No one has yet shown data indicating that an AP that adjusts for exercise using continuously collected physical activity data should perform better than an AP that does not include these data as additional inputs.…”

Section: Results Of Adapting To Exercisementioning

confidence: 99%

Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs

Resalat

Hilts

Youssef

et al. 2019

J Diabetes Sci Technol

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Background: People with type 1 diabetes (T1D) have varying sensitivities to insulin and also varying responses to meals and exercise. We introduce a new adaptive run-to-run model predictive control (MPC) algorithm that can be used to help people with T1D better manage their glucose levels using an artificial pancreas (AP). The algorithm adapts to individuals’ different insulin sensitivities, glycemic response to meals, and adjustment during exercise as a continuous input during free-living conditions. Methods: A new insulin sensitivity adaptation (ISA) algorithm is presented that updates each patient’s insulin sensitivity during nonmeal periods to reduce the error between the actual glucose levels and the process model. We further demonstrate how an adaptive learning postprandial hypoglycemia prevention algorithm (ALPHA) presented in the previous work can complement the ISA algorithm, and the algorithm can adapt in several days. We further show that if physical activity is incorporated as a continuous input (heart rate and accelerometry), performance is improved. The contribution of this work is the description of the ISA algorithm and the evaluation of how ISA, ALPHA, and incorporation of exercise metrics as a continuous input can impact glycemic control. Results: Incorporating ALPHA, ISA, and physical activity into the MPC improved glycemic outcome measures. The adaptive learning postprandial hypoglycemia prevention algorithm combined with ISA significantly reduced time spent in hypoglycemia by 71.7% and the total number of rescue carbs by 67.8% to 0.37% events/day/patient. Insulin sensitivity adaptation significantly reduced model-actual mismatch by 12.2% compared to an AP without ISA. Incorporating physical activity as a continuous input modestly improved time in the range 70 to 180 mg/dL during high physical activity days from 84.4% to 84.9% and reduced the percentage time in hypoglycemia by 23.8% from 2.1% to 1.6%. Conclusion: Adapting postprandial insulin delivery, insulin sensitivity, and adapting to physical exercise in an MPC-based AP systems can improve glycemic outcomes.

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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%

See 1 more Smart Citation

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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Adaptive personalized multivariable artificial pancreas using plasma insulin estimates

Cited by 43 publications

References 58 publications

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Artificial pancreas under stable pulsatile MPC: Improving the closed-loop performance

Adaptive Control of an Artificial Pancreas Using Model Identification, Adaptive Postprandial Insulin Delivery, and Heart Rate and Accelerometry as Control Inputs

The Necessity of Interdisciplinary Software Development for Building Viable Research Platforms

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