Experimental evaluation of a recursive model identification technique for type 1 diabetes

Journal of Diabetes Science and Technology

Volumn 3, Issue 5, 2009, Pages 1192-1202

  Finan, Daniel A ^a,b,c   Doyle III, Francis J ^a,b   Palerm, Cesar C ^a,b,d   Bevier, Wendy C ^b   Zisser, Howard C ^a,b   Jovanovič, Lois ^a,b   Seborg, Dale E ^a,b  

^a University of California   (United States)

^b SANSUM DIABETES RESEARCH INSTITUTE   (United States)

^c TECHNICAL UNIVERSITY OF DENMARK   (Denmark)

^d MEDTRONIC INC   (United States)

Author keywords

Artificial cell; Insulin sensitivity; Linear dynamic models; Recursive estimation; Type 1 diabetes

Indexed keywords

ANTIDIABETIC AGENT; GLUCOCORTICOID; INSULIN; PREDNISONE;

ADULT; ARTICLE; BIOLOGICAL MODEL; BLOOD; BLOOD GLUCOSE MONITORING; CARBOHYDRATE DIET; COMPUTER SIMULATION; DRUG EFFECT; EVALUATION; FEMALE; GLUCOSE BLOOD LEVEL; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; INSULIN INFUSION; MALE; METABOLISM; PANCREAS ISLET BETA CELL; PREDICTION AND FORECASTING; REPRODUCIBILITY; RETROSPECTIVE STUDY; STATISTICAL MODEL; TIME; TREATMENT OUTCOME;

ADULT; BLOOD GLUCOSE; BLOOD GLUCOSE SELF-MONITORING; COMPUTER SIMULATION; DIABETES MELLITUS, TYPE 1; DIETARY CARBOHYDRATES; FEMALE; GLUCOCORTICOIDS; HUMANS; HYPOGLYCEMIC AGENTS; INSULIN; INSULIN INFUSION SYSTEMS; INSULIN-SECRETING CELLS; LINEAR MODELS; MALE; MODELS, BIOLOGICAL; MODELS, STATISTICAL; PREDICTIVE VALUE OF TESTS; PREDNISONE; REPRODUCIBILITY OF RESULTS; RETROSPECTIVE STUDIES; TIME FACTORS; TREATMENT OUTCOME;

EID: 77949883528     PISSN: None     EISSN: 19322968     Source Type: Journal    
DOI: 10.1177/193229680900300526     Document Type: Article

References (18)

1. Finan DA, Zisser H, Jovanovic L, Bevier WC, Seborg DE. Practical issues in the identification of empirical models from simulated type 1 diabetes data. Diabetes Technol Ther. 2007;9(5):438-50. (Pubitemid 47585348)
2. Finan DA, Palerm CC, Doyle FJ 3rd, Zisser H, Jovanovic L, Bevier WC, Seborg DE. Identification of empirical dynamic models from type 1 diabetes subject data. Proc. 2008 Amer Control Conf; Seattle, WA; 2008. p. 2099-104.
3. Finan DA, Palerm CC, Doyle FJ 3rd, Zisser H, Bevier WC, Jovanivic L, Seborg DE. Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes. AIChE J. 2009;55(5):1135-46.
4. Ståhl F, Johansson R. Diabetes mellitus modeling and short-term prediction based on blood glucose measurements. Math Biosci. 2009;217:101-17.
5. Gani A, Gribok AV, Rajaraman S, Ward WK, Reifman J. Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling. IEEE Trans Biomed Eng. 2009;56(2):246-54.
6. Hovorka R. Management of diabetes using adaptive control. Int J Adapt Control Signal Process. 2005;19:309-25. (Pubitemid 40751238)
7. Bequette BW. A critical assessment of algorithms and challenges in the development of a closed-loop artificial pancreas. Diabetes Technol Ther. 2005;7(1):28-47. (Pubitemid 40354574)
8. Bevier WC, Zisser H, Jovanovic L, Finan DA, Palerm CC, Seborg DE, Doyle FJ 3rd. Use of continuous glucose monitoring to estimate insulin requirements in patients with type 1 diabetes mellitus during a short course of prednisone. J Diabetes Sci Technol. 2008;2(4):578-83.
9. Hovorka R, Canonico V, Chassin LJ, Haueter U, Massi-Benedetti M, Orsini Federici M, Pieber TR, Schaller HC, Schaupp L, Vering T, Wilinska ME. Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol Meas. 2004;25(4):905-20. (Pubitemid 39121173)
10. Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti 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):931-7. (Pubitemid 46628560)
11. Eren-Oruklu M, Cinar A, Colmekci C, Camurdan MC. Self-tuning controller for regulation of glucose levels in patients with type 1 diabetes. Proc. 2008 Amer. Control Conf; Seattle, WA; 2008. p. 819-24.
12. 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):243-53.
13. Söderström T, Stoica P. System identification. New York: Prentice Hall; 1989.
14. Montgomery DC, Runger GC. Applied statistics and probability for engineers. 4th ed. New York: John Wiley & Sons, Inc.; 2007.
15. Ljung L. System identification: theory for the user. 2nd ed. New Jersey: Prentice Hall PTR; 1999.
16. Fortescue TR, Kershenbaum LS, Ydstie BE. Implementation of self-tuning regulators with variable forgetting factors. Automatica. 1981;17:831-5. (Pubitemid 12511922)
17. Åström KJ, Wittenmark B. Adaptive control. Reading, MA: Addison-Wesley Publishing Company; 1989.
18. Finan DA. Modeling and monitoring strategies for type 1 diabetes [Ph.D. thesis]. University of California, Santa Barbara; 2008.