Status of creation of hardware-software complex of automatic control of the insulin delivery

Тутубалин, П И; Новикова, Светлана; Semenova, Anastasia; Komissarova, E.G.; Arutyunova, N.; Sotnikov, S.; Alexandrov, A.

doi:10.1088/1742-6596/1368/4/042006

Cited by 6 publications

(1 citation statement)

References 11 publications

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“…The use of old insulin pumps and sensors is required due to their communication protocol simplicity and transparency (with a risk of weak security [ 24 , 25 ]). The OpenAPS is distributed with a rule-based controller called oref0 , although the recently improved version is often referred to as oref1 [ 26 ].…”

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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Section: Introductionmentioning

confidence: 99%