Detail
Article
Online article
FT
Medvik - BMC
  • Something wrong with this record ?

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

M. Ubl, T. Koutny, A. Della Cioppa, I. De Falco, E. Tarantino, U. Scafuri

. 2022 ; 22 (23) : . [pub] 20221202

Language English Country Switzerland

Document type Journal Article

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.

References provided by Crossref.org

000      
00000naa a2200000 a 4500
001      
bmc22032261
003      
CZ-PrNML
005      
20230131150807.0
007      
ta
008      
230120s2022 sz f 000 0|eng||
009      
AR
024    7_
$a 10.3390/s22239445 $2 doi
035    __
$a (PubMed)36502149
040    __
$a ABA008 $b cze $d ABA008 $e AACR2
041    0_
$a eng
044    __
$a sz
100    1_
$a Ubl, Martin $u Department of Computer Science and Engineering, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech Republic $1 https://orcid.org/0000000332690408
245    10
$a Distributed Assessment of Virtual Insulin-Pump Settings Using SmartCGMS and DMMS.R for Diabetes Treatment / $c M. Ubl, T. Koutny, A. Della Cioppa, I. De Falco, E. Tarantino, U. Scafuri
520    9_
$a 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.
650    _2
$a lidé $7 D006801
650    12
$a krevní glukóza $7 D001786
650    12
$a diabetes mellitus 1. typu $7 D003922
650    _2
$a hypoglykemika $x terapeutické užití $7 D007004
650    _2
$a inzulinové infuzní systémy $7 D007332
650    _2
$a inzulin $x terapeutické užití $7 D007328
655    _2
$a časopisecké články $7 D016428
700    1_
$a Koutny, Tomas $u Department of Computer Science and Engineering, New Technologies for Information Society, University of West Bohemia, Technicka 18, 330 01 Pilsen, Czech Republic $1 https://orcid.org/000000027773098X
700    1_
$a Della Cioppa, Antonio $u Natural Computation Lab, Department of Information Engineering, Electrical Engineering and Applied Mathematics, University of Salerno, Via Giovanni Paolo II 132, 84084 Fisciano, Italy $1 https://orcid.org/0000000240926102
700    1_
$a De Falco, Ivanoe $u ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy $1 https://orcid.org/0000000161271195
700    1_
$a Tarantino, Ernesto $u ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy
700    1_
$a Scafuri, Umberto $u ICAR-National Research Council of Italy, Via P. Castellino, 80131 Naples, Italy
773    0_
$w MED00008309 $t Sensors (Basel, Switzerland) $x 1424-8220 $g Roč. 22, č. 23 (2022)
856    41
$u https://pubmed.ncbi.nlm.nih.gov/36502149 $y Pubmed
910    __
$a ABA008 $b sig $c sign $y p $z 0
990    __
$a 20230120 $b ABA008
991    __
$a 20230131150803 $b ABA008
999    __
$a ok $b bmc $g 1891173 $s 1183596
BAS    __
$a 3
BAS    __
$a PreBMC-MEDLINE
BMC    __
$a 2022 $b 22 $c 23 $e 20221202 $i 1424-8220 $m Sensors $n Sensors Basel $x MED00008309
LZP    __
$a Pubmed-20230120

Find record

Citation metrics

Loading data ...

Archiving options

Loading data ...