How much is short-term glucose prediction in type 1 diabetes improved by adding insulin delivery and meal content information to CGM data? A proof-of-concept study

Journal of Diabetes Science and Technology

Volumn 10, Issue 5, 2016, Pages 1149-1160

  Zecchin, Chiara ^a   Facchinetti, Andrea ^a   Sparacino, Giovanni ^a   Cobelli, Claudio ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Continuous glucose monitoring; Neural network; Nonlinear modeling; Sensitivity analysis; Signal processing

Indexed keywords

GLUCOSE; INSULIN; ANTIDIABETIC AGENT; GLUCOSE BLOOD LEVEL;

ALGORITHM; ARTICLE; ARTIFICIAL NEURAL NETWORK; BLOOD GLUCOSE MONITORING; CARBOHYDRATE ANALYSIS; CONTROLLED STUDY; INSULIN DEPENDENT DIABETES MELLITUS; MEAL; MEASUREMENT ACCURACY; MODEL; PREDICTION; PREDICTOR VARIABLE; SENSITIVITY ANALYSIS; TIME FACTOR; ANALYSIS; BLOOD; DIABETES MELLITUS, TYPE 1; GLUCOSE BLOOD LEVEL; HUMAN; INSULIN INFUSION;

ALGORITHMS; BLOOD GLUCOSE; BLOOD GLUCOSE SELF-MONITORING; DIABETES MELLITUS, TYPE 1; HUMANS; HYPOGLYCEMIC AGENTS; INSULIN; INSULIN INFUSION SYSTEMS; MEALS;

EID: 85012982071     PISSN: None     EISSN: 19322968     Source Type: Journal    
DOI: 10.1177/1932296816654161     Document Type: Article

References (34)

1. Hughes CS, Patek SD, Breton MD, Kovatchev BP. Hypoglycemia prevention via pump attenuation and red-yellow-green "traffic" lights using continuous glucose monitoring and insulin pump data. J Diabetes Sci Technol. 2010;4(5):1146-1155.
2. Zecchin C, Facchinetti A, Sparacino G, Cobelli C. Reduction of number and duration of hypoglycemic events by glucose prediction methods: a proof-of-concept in silico study. Diabetes Technol Ther. 2013;15(1):66-77.
3. Buckingham B, Chase HP, Dassau E, et al. Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care. 2010;33(5):1013-1017.
4. Dassau E, Cameron F, Lee H, et al. Real-time hypoglycemia prediction suite using continuous glucose monitoring: a safety net for the artificial pancreas. Diabetes Care. 2010;33(6):1249-1254.
5. Reifman J, Rajaraman S, Gribok A, Ward WK. Predictive monitoring for improved management of glucose levels. J Diabetes Sci Technol. 2007;1(4):478-486.
6. 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):246-254.
7. 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):931-937.
8. 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.
9. Perez-Gandia C, Facchinetti A, Sparacino G, et al. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther. 2010;12(1):81-88.
10. Naumova V, Pereverzyev S, Sivananthan S. A meta-learning approach to the regularized learning-case study: blood glucose prediction. Neural Networks. 2012;33(9):181-193.
11. Finan D, Doyle F, Palerm C, et al. Experimental evaluation of a recursive model identification technique for type 1 diabetes. J Diabetes Sci Technol. 2009;5(3):1192-1202.
12. Castillo-Estrada G, del Re L, Renard E. Nonlinear gain in online prediction of blood glucose profile in type 1 diabetic patients. In: 49th IEEE Conference on Decision and Control (CDC). New York, NY: IEEE; 2010:1668-1673.
13. Eren-Oruklu M, Cinar A, Rollins D, Quinn L. Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms. Automatica. 2012;48(8):1892-1897.
14. Turksoy K, Bayrak ES, Quinn L, Littlejohn E, Rollins D, Cinar A. Hypoglycemia early alarm systems based on multivariable models. Ind Eng Chem Res. 2013;52(35): 1-19.
15. Daskalaki E, Prountzou A, Diem P, Mougiakakou S. G. Realtime adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients. Diabetes Technol Ther. 2012;14(2):168-174.
16. Zhao C, Dassau E, Jovanovic L, Zisser H, Doyle F III, Seborg D. Predicting subcutaneous glucose concentration using a latent-variable-based statistical method for type 1 diabetes mellitus. J Diabetes Sci Technol. 2012;6(3):617-633.
17. Georga E, Protopappas V, Polyzos D, Fotiadis D. A predictive model of subcutaneous glucose concentration in type 1 diabetes based on random forests. In: 34th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). New York, NY: IEEE; 2012:2889-2892.
18. Georga E, Protopappas VC, Ardigo D, et al. Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE J Biomed Health Inform. 2013;17(1):71-81.
19. Georga E, Protopappas V, Polyzos D, Fotiadis D. Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models. Med Biol Eng Comput. 2015;53(12):1305-1318.
20. Pappada S, Cameron B, Rosman P, et al. Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol Ther. 2011;13(2):135-141.
21. Zecchin C, Facchinetti A, Sparacino G, De Nicolao G, Cobelli C. Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration. IEEE Trans Biomed Eng. 2012;59(6):1550-1560.
22. Zecchin C, Facchinetti A, Sparacino G, Cobelli C. Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information. Comput Methods Programs Biomed. 2014;113(1):144-152.
23. Dalla Man C, Camilleri M, Cobelli C. A system model of oral glucose absorption: validation on gold standard data. IEEE Trans Biomed Eng. 2006;53(12 pt 1):2472-2478.
24. Dalla Man C, Raimondo DM, Rizza RA, Cobelli C. GIM, simulation software of meal glucose-insulin model. J Diabetes Sci Technol. 2007;1(3):323-330.
25. Dalla Man C, Caumo A, Basu R, Rizza R, Toffolo G, Cobelli C. Minimal model estimation of glucose absorption and insulin sensitivity from oral test: validation with a tracer method. Am J Physiol Endocrinol Metab. 2004;287(4):E637-E643.
26. Dalla Man C, Rizza RA, Cobelli C. Meal simulation model of the glucose-insulin system. IEEE Trans Biomed Eng. 2007;54(10):1740-1749.
27. Gevrey M, Dimopoulos I, Lek S. Two-way interaction of input variables in the sensitivity analysis of neural network models. Ecological Modelling. 2006;195(1-2):43-50.
28. Shojaeefard M, Akbari M, Tahani M, Farhani F. Sensitivity analysis of the artificial neural network outputs in friction stir lap joining of aluminum to brass. Adv Materials Sci Eng. 2013; 2013:574914.
29. Gani A, Gribok AV, Lu Y, Ward WK, Vigersky RA, Reifman J. Universal glucose models for predicting subcutaneous glucose concentration in humans. IEEE Trans Inf Technol Biomed. 2010;14(1):157-165.
30. Facchinetti A, Sparacino G, Trifoglio E, Cobelli C. A new index to optimally design and compare continuous glucose monitoring glucose prediction algorithms. Diabetes Technol Ther. 2011;13(2):111-119.
31. Chen JW, Christiansen JS, Lauritzen T. Limitations to subcutaneous insulin administration in type 1 diabetes. Diabetes Obes Metab. 2003;5(4):223-233.
32. Zecchin C, Facchinetti A, Sparacino G, Kamath A, Peyser T, Cobelli C. Are insulin and meal information useful for shortand mid-term prediction of glucose concentration? J Diabetes Sci Technol. 2014;8(2 suppl):A127.
33. Zecchin C, Facchinetti A, Sparacino G, et al. Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. Diabetes Technol Ther. 2013;15(10):836-844.
34. Kovatchev BP, Cox DJ, Gonder-Frederick LA, Clarke W. Symmetrization of the blood glucose measurement scale and its applications. Diabetes Care. 1997;20(11): 1655-1658.