Adjustment of basal insulin infusion rate in T1DM by hybrid PSO
Basal insulin infusion rate which should be adjusted to increase or decrease insulin delivery with the varying blood sugar level plays a key role in type 1 diabetes mellitus (T1DM) patients for maintaining the blood glucose level approximately steady within reference range in order to avoid the comp...
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| Published in | Soft computing (Berlin, Germany) Vol. 19; no. 7; pp. 1921 - 1937 |
|---|---|
| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.07.2015
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1432-7643 1433-7479 |
| DOI | 10.1007/s00500-014-1378-6 |
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| Abstract | Basal insulin infusion rate which should be adjusted to increase or decrease insulin delivery with the varying blood sugar level plays a key role in type 1 diabetes mellitus (T1DM) patients for maintaining the blood glucose level approximately steady within reference range in order to avoid the complications developed from diabetes. This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm for solving the basal insulin infusion rate problem. In HPSO, bad experience lesson learning scheme and local search based on chaotic dynamics are proposed to make a good balance between global exploration and local exploitation. Simulation results based on a set of well-known optimization benchmark instances and the basal insulin infusion rate adjustment problem for T1DM demonstrate the effectiveness of the proposed HPSO. In silico tests on standard virtual subject via HPSO show that under nominal condition the blood glucose concentrations could be kept within a range of 80–150 mg/dL within less than 5 days; meanwhile, in case of random variations in meal timings within
±
60 min or meal amounts within
±
75 % deviation from the nominal values, the blood glucose concentrations could be kept within the safe regions. |
|---|---|
| AbstractList | Basal insulin infusion rate which should be adjusted to increase or decrease insulin delivery with the varying blood sugar level plays a key role in type 1 diabetes mellitus (T1DM) patients for maintaining the blood glucose level approximately steady within reference range in order to avoid the complications developed from diabetes. This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm for solving the basal insulin infusion rate problem. In HPSO, bad experience lesson learning scheme and local search based on chaotic dynamics are proposed to make a good balance between global exploration and local exploitation. Simulation results based on a set of well-known optimization benchmark instances and the basal insulin infusion rate adjustment problem for T1DM demonstrate the effectiveness of the proposed HPSO. In silico tests on standard virtual subject via HPSO show that under nominal condition the blood glucose concentrations could be kept within a range of 80–150 mg/dL within less than 5 days; meanwhile, in case of random variations in meal timings within ±60 min or meal amounts within ±75 % deviation from the nominal values, the blood glucose concentrations could be kept within the safe regions. Basal insulin infusion rate which should be adjusted to increase or decrease insulin delivery with the varying blood sugar level plays a key role in type 1 diabetes mellitus (T1DM) patients for maintaining the blood glucose level approximately steady within reference range in order to avoid the complications developed from diabetes. This paper proposes an effective hybrid particle swarm optimization (HPSO) algorithm for solving the basal insulin infusion rate problem. In HPSO, bad experience lesson learning scheme and local search based on chaotic dynamics are proposed to make a good balance between global exploration and local exploitation. Simulation results based on a set of well-known optimization benchmark instances and the basal insulin infusion rate adjustment problem for T1DM demonstrate the effectiveness of the proposed HPSO. In silico tests on standard virtual subject via HPSO show that under nominal condition the blood glucose concentrations could be kept within a range of 80–150 mg/dL within less than 5 days; meanwhile, in case of random variations in meal timings within ± 60 min or meal amounts within ± 75 % deviation from the nominal values, the blood glucose concentrations could be kept within the safe regions. |
| Author | Liu, Bo Wang, Youqing Xie, Hongzhi Lou, Zhijiang |
| Author_xml | – sequence: 1 givenname: Zhijiang surname: Lou fullname: Lou, Zhijiang organization: College of Information Science and Technology, Beijing University of Chemical Technology – sequence: 2 givenname: Bo surname: Liu fullname: Liu, Bo organization: Academy of Mathematics and Systems Science, Chinese Academy of Sciences – sequence: 3 givenname: Hongzhi surname: Xie fullname: Xie, Hongzhi organization: College of Information Science and Technology, Beijing University of Chemical Technology – sequence: 4 givenname: Youqing surname: Wang fullname: Wang, Youqing email: wang.youqing@ieee.org organization: College of Information Science and Technology, Beijing University of Chemical Technology |
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| Cites_doi | 10.1109/TEVC.2008.2009460 10.1016/j.swevo.2011.06.005 10.1109/TBME.2006.883792 10.1109/TBME.2007.893506 10.1016/j.jprocont.2007.07.010 10.2337/diacare.20.11.1655 10.1109/TCBB.2011.126 10.1016/j.jprocont.2010.10.003 10.1089/dia.2010.0029 10.1177/193229681000400522 10.1007/s10462-010-9191-9 10.1016/j.aei.2005.01.004 10.1136/bmj.d1855 10.1109/ICNN.1995.488968 10.1109/MCI.2010.936309 10.1177/193229680900300106 10.1007/s10479-011-0894-3 10.1177/193229680700100303 10.1007/s00500-012-0803-y 10.1109/CI-M.2006.248054 10.1016/j.arcontrol.2012.09.007 10.1002/aic.12081 10.1016/j.cor.2006.12.013 10.1007/978-3-540-78490-6_1 10.1109/4235.985692 10.1016/j.compchemeng.2009.12.010 10.2337/dc14-S014 10.1007/s00500-012-0855-z 10.1016/j.ins.2012.06.003 10.1016/j.chaos.2004.11.095 10.1016/j.ejor.2006.06.046 10.1109/MCI.2011.2176995 10.1109/TSMCB.2009.2015956 10.1109/TBME.2006.872818 10.1109/TBME.2009.2024409 10.1109/TEVC.2011.2132725 10.1109/RBME.2009.2036073 10.1109/TEVC.2003.819944 |
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| Keywords | Glycemic control Particle swarm optimization (PSO) Basal insulin infusion rate optimization Type 1 diabetes mellitus |
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| References | LiuBWangLJinYHAn effective hybrid PSO-based algorithm for flow shop scheduling with limited buffersComput Oper Res2008359279128061144.9038510.1016/j.cor.2006.12.013 Ong Y-S, Lim MH, Chen X (2010) Research frontier-memetic computation–past, present & future. IEEE Comput Intell Mag 5(2):24–36 SochaKDorigoMAnt colony optimization for continuous domainsEur J Oper Res20081853115511731146.90537236175010.1016/j.ejor.2006.06.046 Wang YQ, Zisser H, Dassau E, Jovanovic L, Doyle FJ (2010c) Model predictive control with learning-type set-point: application to artificial pancreatic beta-cell. AIChE J 56(6):1510–1518 Dalla ManCRaimondoDMRizzaRACobelliCGIM, simulation software of meal glucose-insulin modelJ Diabetes Sci Technol20071332333010.1177/193229680700100303 ElbeltagiEHegazyTGriersonDComparison among five evolutionary-based optimization algorithmsAdv Eng Inf2005191435310.1016/j.aei.2005.01.004 RanaSJasolaSKumarRA review on particle swarm optimization algorithms and their applications to data clusteringArtif Intell Rev201135321122210.1007/s10462-010-9191-9 Moussouni F, Brisset S, Brochet P (2008) Comparison of two multi-agent algorithms: ACO and PSO for the optimization of a brushless DC wheel motor. In: Intelligent computer techniques in applied electromagnetics. Springer, Berlin, pp 3–10 Ong YS, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110 ZhanZHZhangJLiYChungHSHAdaptive particle swarm optimizationIEEE Trans Syst Man Cybern Part B Cybern20093961362138110.1109/TSMCB.2009.2015956 DorigoMBirattariMStutzleTAnt colony optimizationIEEE Comput Intell Mag200614283910.1109/CI-M.2006.248054 XuWXGengZQZhuQXGuXBA piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimizationInf Sci2013218851021293.90088298923910.1016/j.ins.2012.06.003 WangYQDassauEDoyleFJClosed-loop control of artificial pancreatic beta-cell in type 1 diabetes mellitus using model predictive iterative learning controlIEEE Trans Biomed Eng201057221121910.1109/TBME.2009.2024409 WangYQDassauEZisserHJovanovicLDoyleFJIIIAutomatic bolus and adaptive basal algorithm for the artificial pancreatic beta-cellDiabetes Technol Ther2010121187988710.1089/dia.2010.0029 Nguyen QH, Ong Y-S, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evol Comput 13(3):604–623 PercivalMWBevierWCWangYDassauEZisserHCJovanovicLDoyleFJ3rdModeling the effects of subcutaneous insulin administration and carbohydrate consumption on blood glucoseJ Diabetes Sci Technol2010451214122810.1177/193229681000400522 Hovorka R, Kumareswaran K, Harris J, Allen JM, Elleri D, Xing DY, Kollman C, Nodale M, Murphy HR, Dunger DB, Amiel SA, Heller SR, Wilinska ME, Evans ML (2011) Overnight closed loop insulin delivery (artificial pancreas) in adults with type 1 diabetes: crossover randomised controlled studies. Br Med J 342:d1855 LiuBWangLLiuYWangSYA unified framework for population-based metaheuristicsAnn Oper Res201118612312621225.9016310.1007/s10479-011-0894-3 LiuBWangLLiuYQianBJinYHAn effective hybrid particle swarm optimization for batch scheduling of polypropylene processesComput Chem Eng201034451852810.1016/j.compchemeng.2009.12.010 CobelliCDalla ManCSparacinoGMagniLDe NicolaoGKovatchevBPDiabetes: models, signals, and controlIEEE Rev Biomed Eng20092549610.1109/RBME.2009.2036073 DeepaSugumaranModel order formulation of a multivariable discrete system using a modified particle swarm optimization approachSwarm Evolut Comput20111420421210.1016/j.swevo.2011.06.005 ClercMKennedyJThe particle swarm—explosion, stability, and convergence in a multidimensional complex spaceIEEE Trans Evol Comput200261587310.1109/4235.985692 OwensCZisserHJovanovicLSrinivasanBBonvinDDoyleFJRun-to-run control of blood glucose concentrations for people with type 1 diabetes mellitusIEEE Trans Biomed Eng2006536996100510.1109/TBME.2006.872818 LiuLZWuFXZhangWJInference of biological S-system using the separable estimation method and the genetic algorithmIEEE/ACM Trans Comput Biol Bioinf201294955965298385310.1109/TCBB.2011.126 DengWChenRHeBLiuYQYinLFGuoJHA novel two-stage hybrid swarm intelligence optimization algorithm and applicationSoft Comput201216101707172210.1007/s00500-012-0855-z LeMNOngYSJinYCSendhoffBA unified framework for symbiosis of evolutionary mechanisms with application to water clusters potential model designIEEE Comput Intell Mag201271203510.1109/MCI.2011.2176995 KovatchevBPCoxDJGonder-FrederickLAClarkeWSymmetrization of the blood glucose measurement scale and its applicationsDiabetes Care199720111655165810.2337/diacare.20.11.1655 Li YY, Xiang RR, Jiao LC, Liu RC (2012) An improved cooperative quantum-behaved particle swarm optimization. Soft Comput 16(6):1061–1069 BequetteBWChallenges and recent progress in the development of a closed-loop artificial pancreasAnnu Rev Control201236225526610.1016/j.arcontrol.2012.09.007 American Diabetes Association (2014) Standards of medical care in diabetes—2014. Diabetes Care 37(Supplement 1):S14–S80 Liu B, Wang L, Jin Y-H, Tang F (2005) Improved particle swarm optimization combined with chaos. Chaos Solitons Fractals 25:1261–1271 Dalla ManCRizzaRACobelliCMeal simulation model of the glucose-insulin systemIEEE Trans Bio-Med Eng200754101740174910.1109/TBME.2007.893506 Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neutral networks, vol 2, Australia, pp 1942–1948 PalermCCZisserHJovanovicLDoyleFIJA run-to-run control strategy to adjust basal insulin infusion rates in type 1 diabetesJ Process Control2008183–425826510.1016/j.jprocont.2007.07.010 Kovatchev BP, Breton M, Man CD, Cobelli C (2009) In silico preclinical trials: a proof of concept in closed-loop control of type 1 diabetes. J Diabetes Sci Technol 3(1):44–55 Chen X, Ong Y-S, Lim M-H, Tan KC (2011) A multi-facet survey on memetic computation. IEEE Trans Evol Comput 15(5):591–607 Dalla ManCCamilleriMCobelliCA system model of oral glucose absorption: validation on gold standard dataIEEE Trans Biomed Eng200653122472247810.1109/TBME.2006.883792 PercivalMWWangYGrosmanBDassauEZisserHJovanovicLDoyleFJDevelopment of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parametersJ Process Control201121339140410.1016/j.jprocont.2010.10.003 Deepa (1378_CR9) 2011; 1 WX Xu (1378_CR37) 2013; 218 K Socha (1378_CR33) 2008; 185 ZH Zhan (1378_CR38) 2009; 39 1378_CR16 C Cobelli (1378_CR5) 2009; 2 1378_CR19 B Liu (1378_CR20) 2008; 35 1378_CR18 B Liu (1378_CR22) 2011; 186 CC Palerm (1378_CR29) 2008; 18 YQ Wang (1378_CR35) 2010; 12 C Dalla Man (1378_CR8) 2007; 54 LZ Liu (1378_CR23) 2012; 9 C Dalla Man (1378_CR7) 2007; 1 B Liu (1378_CR21) 2010; 34 S Rana (1378_CR32) 2011; 35 C Dalla Man (1378_CR6) 2006; 53 1378_CR13 MW Percival (1378_CR31) 2011; 21 1378_CR14 1378_CR36 MW Percival (1378_CR30) 2010; 4 YQ Wang (1378_CR34) 2010; 57 1378_CR27 M Clerc (1378_CR4) 2002; 6 MN Le (1378_CR17) 2012; 7 BP Kovatchev (1378_CR15) 1997; 20 M Dorigo (1378_CR11) 2006; 1 1378_CR1 C Owens (1378_CR28) 2006; 53 1378_CR24 BW Bequette (1378_CR2) 2012; 36 W Deng (1378_CR10) 2012; 16 1378_CR26 1378_CR3 E Elbeltagi (1378_CR12) 2005; 19 1378_CR25 |
| References_xml | – reference: Moussouni F, Brisset S, Brochet P (2008) Comparison of two multi-agent algorithms: ACO and PSO for the optimization of a brushless DC wheel motor. In: Intelligent computer techniques in applied electromagnetics. Springer, Berlin, pp 3–10 – reference: XuWXGengZQZhuQXGuXBA piecewise linear chaotic map and sequential quadratic programming based robust hybrid particle swarm optimizationInf Sci2013218851021293.90088298923910.1016/j.ins.2012.06.003 – reference: DeepaSugumaranModel order formulation of a multivariable discrete system using a modified particle swarm optimization approachSwarm Evolut Comput20111420421210.1016/j.swevo.2011.06.005 – reference: ElbeltagiEHegazyTGriersonDComparison among five evolutionary-based optimization algorithmsAdv Eng Inf2005191435310.1016/j.aei.2005.01.004 – reference: LiuBWangLLiuYWangSYA unified framework for population-based metaheuristicsAnn Oper Res201118612312621225.9016310.1007/s10479-011-0894-3 – reference: BequetteBWChallenges and recent progress in the development of a closed-loop artificial pancreasAnnu Rev Control201236225526610.1016/j.arcontrol.2012.09.007 – reference: ZhanZHZhangJLiYChungHSHAdaptive particle swarm optimizationIEEE Trans Syst Man Cybern Part B Cybern20093961362138110.1109/TSMCB.2009.2015956 – reference: Hovorka R, Kumareswaran K, Harris J, Allen JM, Elleri D, Xing DY, Kollman C, Nodale M, Murphy HR, Dunger DB, Amiel SA, Heller SR, Wilinska ME, Evans ML (2011) Overnight closed loop insulin delivery (artificial pancreas) in adults with type 1 diabetes: crossover randomised controlled studies. Br Med J 342:d1855 – reference: SochaKDorigoMAnt colony optimization for continuous domainsEur J Oper Res20081853115511731146.90537236175010.1016/j.ejor.2006.06.046 – reference: DengWChenRHeBLiuYQYinLFGuoJHA novel two-stage hybrid swarm intelligence optimization algorithm and applicationSoft Comput201216101707172210.1007/s00500-012-0855-z – reference: Ong Y-S, Lim MH, Chen X (2010) Research frontier-memetic computation–past, present & future. IEEE Comput Intell Mag 5(2):24–36 – reference: American Diabetes Association (2014) Standards of medical care in diabetes—2014. Diabetes Care 37(Supplement 1):S14–S80 – reference: Ong YS, Keane AJ (2004) Meta-Lamarckian learning in memetic algorithms. IEEE Trans Evol Comput 8(2):99–110 – reference: LiuBWangLJinYHAn effective hybrid PSO-based algorithm for flow shop scheduling with limited buffersComput Oper Res2008359279128061144.9038510.1016/j.cor.2006.12.013 – reference: Nguyen QH, Ong Y-S, Lim MH (2009) A probabilistic memetic framework. IEEE Trans Evol Comput 13(3):604–623 – reference: Li YY, Xiang RR, Jiao LC, Liu RC (2012) An improved cooperative quantum-behaved particle swarm optimization. Soft Comput 16(6):1061–1069 – reference: PercivalMWWangYGrosmanBDassauEZisserHJovanovicLDoyleFJDevelopment of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parametersJ Process Control201121339140410.1016/j.jprocont.2010.10.003 – reference: LeMNOngYSJinYCSendhoffBA unified framework for symbiosis of evolutionary mechanisms with application to water clusters potential model designIEEE Comput Intell Mag201271203510.1109/MCI.2011.2176995 – reference: LiuBWangLLiuYQianBJinYHAn effective hybrid particle swarm optimization for batch scheduling of polypropylene processesComput Chem Eng201034451852810.1016/j.compchemeng.2009.12.010 – reference: Kennedy J, Eberhart R (1995) Particle swarm optimization. In: Proceedings of IEEE international conference on neutral networks, vol 2, Australia, pp 1942–1948 – reference: Liu B, Wang L, Jin Y-H, Tang F (2005) Improved particle swarm optimization combined with chaos. Chaos Solitons Fractals 25:1261–1271 – reference: LiuLZWuFXZhangWJInference of biological S-system using the separable estimation method and the genetic algorithmIEEE/ACM Trans Comput Biol Bioinf201294955965298385310.1109/TCBB.2011.126 – reference: Dalla ManCRizzaRACobelliCMeal simulation model of the glucose-insulin systemIEEE Trans Bio-Med Eng200754101740174910.1109/TBME.2007.893506 – reference: Dalla ManCCamilleriMCobelliCA system model of oral glucose absorption: validation on gold standard dataIEEE Trans Biomed Eng200653122472247810.1109/TBME.2006.883792 – reference: DorigoMBirattariMStutzleTAnt colony optimizationIEEE Comput Intell Mag200614283910.1109/CI-M.2006.248054 – reference: KovatchevBPCoxDJGonder-FrederickLAClarkeWSymmetrization of the blood glucose measurement scale and its applicationsDiabetes Care199720111655165810.2337/diacare.20.11.1655 – reference: Chen X, Ong Y-S, Lim M-H, Tan KC (2011) A multi-facet survey on memetic computation. 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| SubjectTerms | Algorithms Artificial Intelligence Blood Computational Intelligence Control Diabetes Diabetes mellitus Engineering Glucose Hyperglycemia Hypoglycemia Insulin Mathematical Logic and Foundations Mechatronics Metabolism Methodologies and Application Optimization Particle swarm optimization Random variables Robotics Simulation Velocity |
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| Title | Adjustment of basal insulin infusion rate in T1DM by hybrid PSO |
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