Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

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

doi:10.1089/dia.2017.0364

Cited by 77 publications

(55 citation statements)

References 33 publications

(37 reference statements)

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“…Meal detection is important in AP systems that do not permit manual meal announcements and as a safety system for patients who may forget to enter meal information manually. Turksoy et al have also investigated the development of a meal detection system based on analysis of CGM signals using an unscented Kalman filter and a fuzzy system to estimate the carbohydrates content [ 118 - 120 ]. Their approach was validated in silico with 30 T1D patients using the UVA/Padova simulator, which revealed a sensitivity of 91.3% and an error of 23.1% in carbohydrate estimation; and in vivo using data from 11 T1D patients, which revealed a sensitivity of 93.5% for meals and 68.0% for snacks.…”

Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

360

207

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BackgroundArtificial intelligence methods in combination with the latest technologies, including medical devices, mobile computing, and sensor technologies, have the potential to enable the creation and delivery of better management services to deal with chronic diseases. One of the most lethal and prevalent chronic diseases is diabetes mellitus, which is characterized by dysfunction of glucose homeostasis.ObjectiveThe objective of this paper is to review recent efforts to use artificial intelligence techniques to assist in the management of diabetes, along with the associated challenges.MethodsA review of the literature was conducted using PubMed and related bibliographic resources. Analyses of the literature from 2010 to 2018 yielded 1849 pertinent articles, of which we selected 141 for detailed review.ResultsWe propose a functional taxonomy for diabetes management and artificial intelligence. Additionally, a detailed analysis of each subject category was performed using related key outcomes. This approach revealed that the experiments and studies reviewed yielded encouraging results.ConclusionsWe obtained evidence of an acceleration of research activity aimed at developing artificial intelligence-powered tools for prediction and prevention of complications associated with diabetes. Our results indicate that artificial intelligence methods are being progressively established as suitable for use in clinical daily practice, as well as for the self-management of diabetes. Consequently, these methods provide powerful tools for improving patients’ quality of life.

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Section: Resultsmentioning

confidence: 99%

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Contreras¹,

Vehı́²

2018

J Med Internet Res

360

207

View full text Add to dashboard Cite

show abstract

“…VSD identified meal size 14 and has been applied to real-world data, 21 but it has significant delays and the meal size errors can lead to overestimates of insulin doses. Samadi et al 23 also identified meal size in real-world data, but with a high standard deviation in errors (28 g, roughly the amount of carbohydrates in a medium potato or banana). Further, that work evaluated meal size using the set of carbohydrate estimates within 2 hours of the start of the meal, making it difficult to compare results to methods that identify the meal and its quantity at the same time.…”

Section: Introductionmentioning

confidence: 99%

Automated meal detection from continuous glucose monitor data through simulation and explanation

Zheng

Kleinberg

2019

Journal of the American Medical Informatics Association

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Background Artificial pancreas systems aim to reduce the burden of type 1 diabetes by automating insulin dosing. These systems link a continuous glucose monitor (CGM) and insulin pump with a control algorithm, but require users to announce meals, without which the system can only react to the rise in blood glucose. Objective We investigate whether CGM data can be used to automatically infer meals in daily life even in the presence of physical activity, which can raise or lower blood glucose. Materials and Methods We propose a novel meal detection algorithm that combines simulations with CGM, insulin pump, and heart rate monitor data. When observed and predicted glucose differ, our algorithm uses simulations to test whether a meal may explain this difference. We evaluated our method on simulated data and real-world data from individuals with type 1 diabetes. Results In simulated data, we detected meals earlier and with higher accuracy than was found in prior work (25.7 minutes, 1.2 g error; compared with 48.3 minutes, 17.2 g error). In real-world data, we discovered a larger number of plausible meals than was found in prior work (30 meals, 76.7% accepted; compared with 33 meals, 39.4% accepted). Discussion Prior research attempted meal detection from CGM, but had delays and lower accuracy in real data or did not allow for physical activity. Our approach can be used to improve insulin dosing in an artificial pancreas and trigger reminders for missed meal boluses. Conclusions We demonstrate that meal information can be robustly inferred from CGM and body-worn sensor data, even in challenging environments of daily life.

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“…The adaptive and personalized PIC estimator is able to provide accurate estimates of the insulin present in the bloodstream for direct use in the control algorithm. The presented results are based on an MPC controller without incorporating any additional AP modules like the meal detection and carbohydrate estimation module that automatically recognizes carbohydrate consumption and suggests appropriate boluses [38,[47][48][49]. Such modules have the potential to further improve the closed-loop performance of the proposed PIC-cognizant MPC for use in safe and reliable AP systems.…”

Section: Discussionmentioning

confidence: 99%

Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

Hajizadeh

Rashid

Çınar

2019

Journal of Process Control

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An adaptive model predictive control (MPC) algorithm with dynamic adjustments of constraints and objective function weights based on estimates of the plasma insulin concentration (PIC) is proposed for artificial pancreas (AP) systems. A personalized compartment model that translates the infused insulin into estimates of PIC is integrated with a recursive subspace-based system identification to characterize the transient dynamics of glycemic measurements. The system identification approach is able to identify stable, reliable linear time-varying models from closedloop data. An MPC algorithm using the adaptive models is designed to compute the optimal exogenous insulin delivery for AP systems without requiring any manually-entered meal information. A dynamic safety constraint derived from the estimation of PIC is incorporated in the adaptive MPC to improve the efficacy of the AP and prevent insulin overdosing. Simulation case studies demonstrate the performance of the proposed adaptive MPC algorithm.

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Automatic Detection and Estimation of Unannounced Meals for Multivariable Artificial Pancreas System

Abstract: Integration of a meal detection module in an AP system is a further step toward an automated AP without manual entries. Detection of a consumed meal/snack and infusion of insulin boluses using an estimate of CHO enables the AP system to automatically prevent postprandial hyperglycemia.

Cited by 77 publications

References 33 publications

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Artificial Intelligence for Diabetes Management and Decision Support: Literature Review

Automated meal detection from continuous glucose monitor data through simulation and explanation

Plasma-insulin-cognizant adaptive model predictive control for artificial pancreas systems

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