A data driven nonlinear stochastic model for blood glucose dynamics

Computer Methods and Programs in Biomedicine

Volumn 125, Issue , 2016, Pages 18-25

  Zhang, Yan ^a   Holt, Tim A ^b   Khovanova, Natalia ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY OF OXFORD   (United Kingdom)

Author keywords

Blood glucose dynamics; Data driven models; Diabetes mellitus; Nonlinear systems; Stochastic systems; System identification

Indexed keywords

BLOOD; DIAGNOSIS; DIFFERENTIAL EQUATIONS; DYNAMICS; FREE ENERGY; GLUCOSE; IDENTIFICATION (CONTROL SYSTEMS); INSULIN; NONLINEAR EQUATIONS; NONLINEAR SYSTEMS; SPEECH ENHANCEMENT; STOCHASTIC CONTROL SYSTEMS; STOCHASTIC SYSTEMS;

BLOOD GLUCOSE; BLOOD GLUCOSE CONCENTRATION; CONTINUOUS GLUCOSEMONITORING (CGM); DATA-DRIVEN MODEL; DIABETES MELLITUS; NONLINEAR STOCHASTIC MODEL; SECOND-ORDER DIFFERENTIAL EQUATION; VARIATIONAL BAYESIAN LEARNING;

STOCHASTIC MODELS;

GLICLAZIDE; GLUCOSE; HEMOGLOBIN A1C; INSULIN ASPART; INSULIN GLARGINE; METFORMIN; ROSIGLITAZONE; GLUCOSE BLOOD LEVEL;

ADULT; AGED; ARTICLE; BAYESIAN LEARNING; BLOOD GLUCOSE MONITORING; CLINICAL ARTICLE; CONTROLLED STUDY; DIABETES MELLITUS; FEMALE; FOOD INTAKE; GLUCOSE BLOOD LEVEL; HUMAN; INSULIN DEPENDENT DIABETES MELLITUS; INSULIN PUMP; MALE; MIDDLE AGED; NON INSULIN DEPENDENT DIABETES MELLITUS; NONLINEAR SYSTEM; STOCHASTIC MODEL; ANALYSIS; BLOOD; CASE CONTROL STUDY; MARKOV CHAIN;

ADULT; AGED; BLOOD GLUCOSE; CASE-CONTROL STUDIES; DIABETES MELLITUS, TYPE 1; FEMALE; HUMANS; MALE; MIDDLE AGED; NONLINEAR DYNAMICS; STOCHASTIC PROCESSES;

EID: 84958941005     PISSN: 01692607     EISSN: 18727565     Source Type: Journal    
DOI: 10.1016/j.cmpb.2015.10.021     Document Type: Article

References (29)

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. 1994, 286-287.
2. Hovorka R. Closed-loop insulin delivery: from bench to clinical practice. Nat. Rev. Endocrinol. 2011, 7(7):385-395.
3. Ruan Y., Thabit H., Wilinska M.E., Hovorka R. Modelling endogenous insulin concentration in type 2 diabetes during closed-loop insulin delivery. BioMed. Eng. Online 2015, 14(1):19.
4. Bergman R.N. Lilly lecture 1989. Toward physiological understanding of glucose tolerance. Minimal-model approach. Diabetes 1989, 38(12):1512-1527.
5. Palumbo P., Ditlevsen S., Bertuzzi A., De Gaetano A. Mathematical modeling of the glucose-insulin system: a review. Math. Biosci. 2013, 244(2):69-81.
6. De Gaetano A., Arino O. Mathematical modelling of the intravenous glucose tolerance test. J. Math. Biol. 2000, 40(2):136-168.
7. Watson E.M., Chappell M.J., Ducrozet F., Poucher S.M., Yates J.W.T. A new general glucose homeostatic model using a proportional-integral-derivative controller. Comput. Methods Progr. Biomed. 2011, 102(2):119-129.
8. Dalla Man C., Rizza R.a., Cobelli C. Mixed meal simulation model of glucose-insulin system. Conf. Proc.: Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2006, 1(10):307-310.
9. Marmarelis V., Mitsis G. Data-driven Modeling for Diabetes, ser. Lecture Notes in Bioengineering 2014, Springer, Berlin; Heidelberg. V. Marmarelis, G. Mitsis (Eds.).
10. Florian J., Parker R.S. Empirical Modeling for Glucose Control in Critical Care and Diabetes. Eur. J. Control 2005, 11(6):601-616.
11. Trajanoski Z., Regittnig W., Wach P. Simulation studies on neural predictive control of glucose using the subcutaneous route. Comput. Methods Progr. Biomed. 1998, 56(2):133-139.
12. Cobelli C., Dalla Man C., Sparacino G., Magni L., De Nicolao G., Kovatchev B.P. Diabetes: models, signals, and control. IEEE Rev. Biomed. Eng. 2009, 2:54-96.
13. Makroglou A., Li J., Kuang Y. Mathematical models and software tools for the glucose-insulin regulatory system and diabetes: an overview. Appl. Numer. Math. 2006, 56(3-4):559-573.
14. Georga E.I., Protopappas V.C., Fotiadis D.I. Glucose Prediction in type 1 and type 2 diabetic patients using data driven techniques. Knowl. Oriented Appl. Datamin. 2011, 277-296.
15. Wu H.-i. A case study of type 2 diabetes self-management. Biomed. Eng. Online 2005, 4:4.
16. Kirchsteiger H., Johansson R., Renard E., Re L.D. Continuous-time interval model identification of blood glucose dynamics for type 1 diabetes. Int. J. Control 2014, 1-13.
17. Khovanova N.A., Khovanov I.A., Sbano L., Griffiths F., Holt T.A. Characterization of linear predictability and non-stationarity of subcutaneous glucose profiles. Comput. Methods Progr. Biomed. 2013, 110:260-267.
18. Minimed M., City R. Continuous glucose monitoring. Arch. Dis. Childh. 2005, 28(5):150.
19. Friston K., Stephan K., Li B., Daunizeau J. Generalised filtering. Math. Probl. Eng. 2010, 2010:1-34.
20. Cescon M., Johansson R., Renard E., Maran A. Identification of individualised empirical models of carbohydrate and insulin effects on T1DM blood glucose dynamics. Int. J. Control 2014, 87(7):1438-1453.
21. Beal M.J. Variational algorithms for approximate Bayesian inference 2003, (PhD dissertation).
22. Toolbox SPM. The toolbox is an extention of SPM package developed at UCL, UK. [Online]. Available: http://www.fil.ion.ucl.ac.uk/spm/.
23. Spiegelhalter D.J., Abrams K.R., Myles J.P. Bayesian Approaches to Clinical Trials and Health-Care Evaluation 2004, Wiley, Chichester.
24. Daunizeau J., Friston K.J., Kiebel S.J. Variational Bayesian identification and prediction of stochastic nonlinear dynamic causal models. Physica D: Nonlinear Phenom. 2009, 238(21):2089-2118.
25. Rangel C., Angus J., Ghahramani Z., Wild D. Modeling genetic regulatory networks using gene expression profiling and state-space models. Probabilistic Modeling in Bioinformatics and Medical Informatics SE - 9 2005, 269-293. Springer, London.
26. Hines K.E., Middendorf T.R., Aldrich R.W. Determination of parameter identifiability in nonlinear biophysical models: a Bayesian approach. J. Gen. Physiol. 2014, 143(3):401-416.
27. Kass R.R.E., Raftery A.E.A. Bayes factors. J. Am. Stat. Assoc. 1995, 90(430):773-795.
28. Nuttall F.Q., Gannon M.C., Wald J.L., Ahmed M., Frank M.A., Nuttall Q., Gannon Marry C., Wald Jean L. Plasma glucose and insulin profiles in normal subjects ingesting diets of varying carbohydrate, fat, and protein content. J. Am. Coll. Nutr. 1985, 4(4):437-450.
29. Parker R.S., Iii F.J.D., Ward J.H., Peppas N.A. Robust H Glucose control in diabetes using a physiological model. Bioeng. Food Nat. Prod. 2000, 46(12).