Closed-loop MPC-based artificial pancreas: Handling circadian variability of insulin sensitivity

Abuin, Pablo; Sereno, Juan E.; Ferramosca, Antonio; González, Alejandro H.

doi:10.23919/aadeca49780.2020.9301636

Cited by 2 publications

(1 citation statement)

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“…In addition, the controller can increase the basal insulin delivery rate to deal with potential or ongoing hyperglycemic episodes. Examples include various proportional(-integrative)-derivative (PD or PID) controllers [ 11 , 12 ], model-predictive control-based (MPC) algorithms [ 13 , 14 ], fuzzy logic controllers [ 15 ], and others.…”

Section: Introductionmentioning

confidence: 99%

Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment

Ubl

Koutný

Cioppa

et al. 2022

Sensors

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Diabetes is a heterogeneous group of diseases that share a common trait of elevated blood glucose levels. Insulin lowers this level by promoting glucose utilization, thus avoiding short- and long-term organ damage due to the elevated blood glucose level. A patient with diabetes uses an insulin pump to dose insulin. The pump uses a controller to compute and dose the correct amount of insulin to keep blood glucose levels in a safe range. Insulin-pump controller development is an ongoing process aiming at fully closed-loop control. Controllers entering the market must be evaluated for safety. We propose an evaluation method that exploits an FDA-approved diabetic patient simulator. The method evaluates a Cartesian product of individual insulin-pump parameters with a fine degree of granularity. As this is a computationally intensive task, the simulator executes on a distributed cluster. We identify safe and risky combinations of insulin-pump parameter settings by applying the binomial model and decision tree to this product. As a result, we obtain a tool for insulin-pump settings and controller safety assessment. In this paper, we demonstrate the tool with the Low-Glucose Suspend and OpenAPS controllers. For average ± standard deviation, LGS and OpenAPS exhibited 1.7 ± 0.6% and 3.2 ± 1.8% of local extrema (i.e., good insulin-pump settings) out of all the entire Cartesian products, respectively. A continuous region around the best-discovered settings (i.e., the global extremum) of the insulin-pump settings spread across 4.0 ± 1.1% and 4.1 ± 1.3% of the Cartesian products, respectively.

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