Identification of empirical dynamic models from type 1 diabetes subject data

Proceedings of the American Control Conference

Volumn , Issue , 2008, Pages 2099-2104

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

^a University of California   (United States)

^b SANSUM DIABETES RESEARCH INSTITUTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC MODELLING; LINEAR DYNAMIC MODELS; PREDICTION HORIZON; TYPE 1 DIABETES;

FORECASTING;

EID: 52449132426     PISSN: 07431619     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ACC.2008.4586802     Document Type: Conference Paper

References (13)

1. The Diabetes Control and Complications Trial (DCCT) 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., vol. 329, 1993, pp. 977-986.
2. Centers for Disease Control and Prevention, National Diabetes Fact Sheet: General Information and National Estimates on Diabetes in the United States, 2005, U.S. Department of Health and Human Services, Atlanta, GA, Tech. Rep., 2005.
3. B.W. Bequette, A Critical Assessment of Algorithms and Challenges in the Development of a Closed-Loop Artificial Pancreas, Diabetes Technol. Ther., vol. 7, 2005, pp. 28-47.
4. R.S. Parker and F.J. Doyle III, Control-Relevant Modeling in Drug Delivery, Adv. Drug Deliv. Rev., vol. 48, 2001, pp. 211-228.
5. R. Bellazzi, C. Siviero, M. Stefanelli, and G. De Nicolao, Adaptive Controllers for Intelligent Monitoring, Artif. Intell. Med., vol. 7, 1995, pp. 515-540.
6. T. Bremer and D.A. Gough, Is Blood Glucose Predictable from Previous Values?, Diabetes, vol. 48, 1999, pp. 445-451.
7. R. Hovorka, V. Canonico, L.J. Chassin, U. Haueter, M. Massi-Benedetti, M.O. Federici, T.R. Pieber, H.C. Schaller, L. Schaupp, T. Vering, and M.E. Wilinska, Nonlinear Model Predictive Control of Glucose Concentration in Subjects With Type 1 Diabetes, Physiol. Meas., vol. 25, 2004, pp. 905-920.
8. G. Sparacino, F. Zanderigo, S. Corazza, A. Maran, A. Facchinetti, and C. Cobelli, Glucose Concentration Can Be Predicted Ahead in Time from Continuous Glucose Monitoring Sensor Time-Series, IEEE Trans. Biomed. Eng., vol. 54, 2007, pp. 931-937.
9. J. Reifman, S. Rajaraman, A. Gribok, and W.K. Ward, Predictive Monitoring for Improved Management of Glucose Levels, J. Diabetes Sci. Technol., vol. 1, 2007, pp. 478-486.
10. R. Hovorka, F. Shojaee-Moradie, P.V. Carroll, L.J. Chassin, I.J. Gowrie, N.C. Jackson, R.S. Tudor, A.M. Umpleby, and R.H. Jones, Partitioning Glucose Distribution/Transport, Disposal, and Endogenous Production During IVGTT, Am. J. Physiol. Endocrinol. Metab., vol. 282, 2002, pp. E992-E1007.
11. L. Ljung, System Identification: Theory for the User, 2nd ed., Prentice Hall PTR, Upper Saddle River, NJ; 1999.
12. D.S. Shook, C. Mohtadi, and S.L. Shah, A Control-Relevant Identification Strategy for GPC, IEEE Trans. Automat. Control, vol. 37, 1992, pp. 975-980.
13. D.S. Shook, C. Mohtadi, and S.L. Shah, Identification for Long-Range Predictive Control, IEE Proc. D, vol. 138, 1991, pp. 75-84.