Multiple-input subject-specific modeling of plasma glucose concentration for feedforward control

Industrial and Engineering Chemistry Research

Volumn 53, Issue 47, 2014, Pages 18216-18225

  Kotz, Kaylee ^a   Cinar, Ali ^b   Mei, Yong ^a   Roggendorf, Amy ^a   Littlejohn, Elizabeth ^c   Quinn, Laurie ^d   Rollins, Derrick K ^a,e  

^a Iowa State University of Science and Technology   (United States)

^b Illinois Institute of Technology   (United States)

^c UNIVERSITY OF CHICAGO MEDICAL CENTER   (United States)

^d UNIVERSITY OF ILLINOIS AT CHICAGO   (United States)

^e Department of Statistics Center for Statistics and Applications in Forensic Evidence Iowa State University 2438 Osborn Dr Ames   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL ORGANS; AUTOMATION; FOOD SUPPLY; GLUCOSE; INSULIN;

ARTIFICIAL PANCREAS; BLOOD GLUCOSE CONCENTRATION; CAUSATION MODEL; FOOD CONSUMPTION; INSULIN-DEPENDENT DIABETES; PHYSIOLOGICAL STRESS; SUBJECT SPECIFIC MODELING; SUBJECT-SPECIFIC;

FEEDFORWARD CONTROL;

EID: 84913555835     PISSN: 08885885     EISSN: 15205045     Source Type: Journal    
DOI: 10.1021/ie404119b     Document Type: Article

References (34)

1. The Diabetes Control and Complications Trial Research Group. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl. J. Med. 1993, 329, 977-986.
2. Ohkubo, Y.; Kishikawa, H.; Araki, E.; et al. Intensive insulin therapy prevents the progression of diabetic microvascular complications in Japanese patients with non-insulin-dependent diabetes mellitus: a randomized prospective 6-year study. Diabetes Res. Clin. Pract. 1995, 28, 103-117.
3. UK Prospective Diabetes Study (UKPDS) group. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33). Lancet 1998, 352, 837-853.
4. UK Prospective Diabetes Study (UKPDS) group. Effect of intensive blood-glucose control with metformin on complications in overweight patients with type 2 diabetes (UKPDS 34). Lancet 1998, 352, 854-865.
5. Hermanides, J.; Phillip, M.; DeVries, J. H. Current application of continuous glucose monitoring in the treatment of diabetes: Pros and cons. Diabetes Care 2011, 34 (2), 197-201.
6. Chan, J. C. N. Diabetes and noncommunicable disease: Prevent the preventables. JAMA, J. Am. Med. Assoc. 2013, 310 (9), 916.
7. Bequette, B. W. A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. Diabetes Technol. Ther. 2005, 7 (1), 28-47.
8. Hovorka, R.; Canonico, V.; Chassin, L. J.; Haueter, U.; Massi-Benedetti, M.; Federici, M. O.; Pieber, T. R.; Schaller, H. C.; Schaupp, L.; Vering, T.; Willinska, M.E. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Meas. 2004, 25, 905-920.
9. Hovorka, R. Continuous glucose monitoring and closed-loop systems. Diabetic Med. 2005, 23, 1-12.
10. Steil, G. M.; Clark, B.; Kanderian, S.; Rebrin, K. Modeling insulin action for development of a closed-loop artificial pancreas. Diabetes Technol. Ther. 2005, 7 (1), 94-108.
11. Magni, L.; Raimondo, D. M.; Bossi, L.; Dalla Man, C.; De Nicolao, G.; Kovatchev, B.; Cobelli, C. Model predictive control of type 1 diabetes: An in silico trial. J. Diabetes Sci. Technol. 2007, 1, 804-812.
12. Weinzimer, S. A.; Steil, G. M.; Garry, M.; Swan, K. L.; Dziura, J.; Kurtz, N.; Tamborlane, W. V. Fully automated closed-loop insulin delivery versus semiautomated hybrid control in pediatric patients with type 1 diabetes using an artificial pancreas. Diabetes Care 2008, 31 (5), 934-936.
13. Hovorka, R.; Allen, J. M.; Elleri, D.; Chassin, L. J.; Harris, J.; Xing, D.; et al. Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: A phase 2 randomised crossover trial. Lancet 2010, 375, 743-51.
14. Kovatchev, B.; Cobelli, C.; Renard, E.; Anderson, S.; Breton, M.; Patek, S.; Clarke, W.; et al. Multinational study of subcutaneous model-predictive closed-loop control in type 1 diabetes mellitus: Summary of the results. J. Diabetes Sci. Technol. 2010, 4 (6), 1374-1381.
15. Cobelli, C.; Renard, E.; Kovatchev, B. Artificial pancreas: Past, present, future. Diabetes 2011, 60, 2672-2682.
16. Berlin, K. S.; Rabideau, E. M.; Hains, A. A. Empirically derived patterns of perceived stress among youth with type 1 diabetes and relationships to metabolic control. J. Pediatr. Psychol. 2012, 37 (9), 990-998.
17. Mommersteeg, P. M. C.; Herr, R.; Zijlstra, W. P.; Schneider, S.; Pouwer, F. Higher levels of psychological distress are associated with a higher risk of incident diabetes during 18 year follow-up: Results from the British household panel survey. BMC Public Health. 2012, 12, 1109.
18. Cagampang, F. R.; Bruce, K. D. The role of the circadian clock system in nutrition and metabolism. Br. J. Nutr. 2012, 108 (3), 381-392.
19. Van Cauter, E.; Polonsky, K. S.; Scheen, A. J. Roles of circadian rhythmicity and sleep in human glucose regulation. Endocr Rev. 1997, 18 (5), 716.
20. Sparacino, G.; Zanderigo, F.; Corazza, S.; Maran, A.; Facchineti, A.; Cobelli, C. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans. Biomed. Eng. 2007, 54 (5), 931-937.
21. Eren-Oruklu, M.; Cinar, A.; Quinn, L.; Smith, D. Estimation of future glucose concentrations with subject-specific recursive linear models. Diabetes Technol. Ther. 2009, 11 (4), 243-253.
22. Gani, A.; Gribok, A. V.; Rajaraman, S.; Ward, W. K.; Reifman, J. Predicting subcutaneous glucose concentration in humans: Datadriven glucose modelling. IEEE Trans. Biomed. Eng. 2009, 56 (2), 246-254.
23. Perez-Gandia, C.; Facchinetti, A.; Sparacino, G.; Cobelli, C.; Gomez, E. J.; Rigla, M.; de Leiva, A.; Hernando, M. E. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol. Ther. 2010, 12 (1), 81-88.
24. Mougiakakou, S. G.; Prountzou, A.; Iliopoulou, D.; Nikita, K. S.; Vazeou, A.; Bartsocas, C. S. Neural network based glucose-insulin metabolism models for children with type 1 diabetes. IEEE Eng. Med. Biol. Soc. 2006, 3545-3548.
25. Pappada, S. M.; Cameron, B. D.; Rosman, P. M.; Bourey, A. E.; Papadimos, T. J. Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol. Ther. 2011, 13 (2), 135-141.
26. Georga, E. I.; Protopappas, V. C.; Ardig'o, D.; Marina, M.; Zavaroni, I.; Polyzos, D.; Fotiadis, D. I. Multivariate prediction of subcutaneous glucose concentration in Type 1 diabetes patients based on support vector regression. IEEE J. Biomed. Health Inf. 2013, 17 (1), 71-81.
27. Rollins, D. K.; Bhandari, N.; Kleindler, J.; Kotz, K.; Strohbehn, A.; Boland, L.; Murphy, M.; Andre, D.; Vyas, N.; Welk, G.; Franke, W. E. Free-living inferential modeling of blood glucose level using only noninvasive inputs. J. Process Control 2010, 20 (1), 95-107.
28. Rollins, D. K.; Bhandari, N. Constrained MIMO dynamic discrete-time modeling exploiting optimal experimental design. J. Process Control 2004, 14 (6), 671-683.
29. Rollins, D. K.; Bhandari, N.; Kotz, K. Critical modeling issues for successful feedforward control of blood glucose in insulin dependent diabetics. American Control Conference, Seattle, WA, June 11-13, 2008.
30. Andre, D.; Teller, A. Health care anywhere today. Stud. Health Technol. Inf. 2005, 118, 89-110.
31. Welk, G.; Blair, S. N.; Woof, K.; Jones, S.; Thompson, R. W. A comparative evaluation of three accelerometry-based physical activity monitors. Med. Sci. Sports Exercise 2000, 32 (9), 489-497.
32. Bates, D. M.; Watts, D. G. Nonlinear Regression Analysis and Its Applications; Wiley: New York, 1988.
33. Garnier, H.; Gilson, M.; Young, P. C.; Huselstein, E. An optimal IV technique for identifying continuous-time transfer function model of multiple input systems. Control Eng. Pract. 2007, 15, 471-486.
34. Beverlin, L. P.; Rollins, D. K.; Vyas, N.; Andre, D. An algorithm for optimally fitting a Wiener model. Math. Probl. Eng. 2011, 2011, Article ID: 570509.