Abstract
Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.
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References
De Gooijer J, Hyndman R (2006) 25 years of time series forecasting. Int J Forecasting 22(3):443–473
Kruppa J, Ziegler A, Knig I (2012) Risk estimation and risk prediction using machine learning methods. Hum Genet 131(7):1–16
Iasemidis L (2011) Seizure prediction and its applications. Neurosurg Clin N Am 22(4):489–513
Bequette BW (2010) Continuous glucose monitoring: real-time algorithms for calibration, filtering, and alarms. J Diabetes Sci Technol 4(2):404
Sparacino G, Zanon M, Facchinetti A et al (2012) Italian contributions to the development of continuous glucose monitoring sensors for diabetes management. Sensors 12(10):13753–13780
Sparacino G, Facchinetti A, Maran A et al (2008) Continuous glucose monitoring time series and hypo/hyperglycemia prevention: requirements, methods, open problems. Cur Diabetes Rev 4(3):181–192
Sparacino G, Facchinetti A, Cobelli C (2010) “Smart” continuous glucose monitoring sensors: on-line signal processing issues. Sensors 10(7):6751–6772
Buckingham B, Cobry E, Clinton P et al (2009) Preventing hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Technol Ther 11(2):93–97
Buckingham B, Chase HP, Dassau E et al (2010) Prevention of nocturnal hypoglycemia using predictive alarm algorithms and insulin pump suspension. Diabetes Care 33(5):1013–1017
Bode B, Gross K, Rikalo N et al (2004) Alarms based on real-time sensor glucose values alert patients to hypo-and hyperglycemia: the guardian continuous monitoring system. Diabetes Technol Ther 6(2):105–113
Garcia A, Rack-Gomer AL, Bhavaraju NC et al (2012) Dexcom G4AP: an advanced continuous glucose monitor for the artificial pancreas. J Diabetes Sci Technol 7(6):1436–1445
Cobelli C, Renard E, Kovatchev B (2011) Artificial pancreas: past, present, future. Diabetes 60(11):2672–2682
Thabit H, Hovorka R (2012) Closed-loop insulin delivery in type 1 diabetes. Endocrinol Metab Clin North Am 41(1):105–117
Zecchin C, Facchinetti A, Sparacino G et al (2014) Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information. Comput Methods Programs Biomed 113(1):144–152
Cryer PE (2007) Hypoglycemia, functional brain failure, and brain death. J Clin Invest 117(4):868–870
Williams G, John CP (2004) Handbook of diabetes. Blackwell, Oxford
The American Diabetes Association (2013) Standards of medical care in diabetes: 2013. Diabetes Care 36(S1):S11–S66
Tamborlane WV, Beck RW, Bode BW (2008) Continuous glucose monitoring and intensive treatment of type 1 diabetes. N Engl J Med 359(10):1464–1476
Battelino T, Phillip M, Bratina N et al (2011) Effect of continuous glucose monitoring on hypoglycemia in type 1 diabetes. Diabetes Care 34(4):795–800
Deiss D, Bolinder J, Riveline JP et al (2006) Improved glycemic control in poorly controlled patients with type 1 diabetes using real-time continuous glucose monitoring. Diabetes Care 29(12):2730–2732
Reifman J, Rajaraman S, Gribok A et al (2007) Predictive monitoring for improved management of glucose levels. J Diabetes Sci Technol 1(4):478–486
Gani A, Gribok AV, Rajaraman J et al (2009) Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling. IEEE Trans Biomed Eng 56(2):246–254
Sparacino G, Zanderigo F, Corazza S et al (2007) Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans Biomed Eng 54(5):931–937
Eren-Oruklu M, Cinar A, Quinn L et al (2009) Estimation of the future glucose concentrations with subject specific recursive linear models. Diabetes Technol Ther 11(4):243–253
Finan DA, Doyle FJ, Palerm CC et al (2009) Experimental evaluation of a recursive model identification technique for type 1 diabetes. J Diabetes Sci Technol 5(3):1192–1202
Castillo-Estrada G, del Re L, Renard E (2010) Nonlinear gain in online prediction of blood glucose profile in type 1 diabetic patients. 49th IEEE Conference on Decision and Control (CDC), p 1668–1673
Eren-Oruklu M, Cinar A, Rollins DK et al (2012) Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms. Automatica 48(8):1892–1897
Turksoy K, Bayrak ES, Quinn L et al (2013) Hypoglycemia early alarm systems based on multivariable models. Ind Eng Chem Res 52:12329–12336
Pérez-Gandía C, Facchinetti A, Sparacino G et al (2010) Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther 12(1):81–88
Pappada SM, Cameron BD, Rosman PM et al (2011) Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol Ther 13(2):135–141
Daskalaki E, Prountzou A, Diem P et al (2012) Real-time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients. Diabetes Technol Ther 14(2):168–174
Zecchin C, Facchinetti A, Sparacino G et al (2012) Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration. IEEE Trans Biomed Eng 59(6):1550–1560
McNelis PD (2005) Neural networks in finance: gaining predictive edge in the market. Elsevier Academic Press, London
Dalla Man C, Camilleri M, Cobelli C (2006) A system model of oral glucose absorption: validation on gold standard data. IEEE Trans Biomed Eng 53(12):2472–2478
Dalla Man C, Rizza RA, Cobelli C (2007) Meal simulation model of the glucose insulin system. IEEE Trans Biomed Eng 54(10):1740–1749
Facchinetti A, Sparacino G, Cobelli C (2011) Online denoising method to handle intraindividual variability of signal-to-noise ratio in continuous glucose monitoring. IEEE Trans Biomed Eng 58(9):2664–2671
Basheer IA, Hajmeer M (2000) Artificial neural networks: fundamentals, computing, design, and application. J Microbiol Methods 43(1):3
S. Amari, N. Murata, K.-R. Muller, M. Finke, H. Yang (1996) Statistical Theory of Overtraining - Is Cross-Validation Asymptotically Effective?, Advances in Neural Information Processing Systems 8, Proceedings of the 1995 Conference, edited by David S. Touretzky, Michael C. Mozer and Michael E. Hasselmo pp 176–182
DIAdvisor. Personal glucose predictive diabetes advisor. http://www.diadvisor.eu/. Accessed 22 Jan 2014
Gani A, Gribok AV, Lu Y et al (2010) Universal glucose models for predicting subcutaneous glucose concentration in humans. IEEE Trans Inf Technol Biomed 14(1):157–165
Facchinetti A, Sparacino G, Cobelli C (2010) Modeling the error of continuous glucose monitoring sensor data: critical aspects discussed through simulation studies. J Diabetes Sci Technol 4(1):4–14
Manohar C, Levine JA, Nandy DK et al (2012) The effect of walking on postprandial glycemic excursion in patients with type 1 diabetes and healthy people. Diabetes Care 35(12):2493–2499
Zecchin C, Facchinetti A, Sparacino G et al (2013) Physical activity measured by physical activity monitoring system correlates with glucose trends reconstructed from continuous glucose monitoring. Diabetes Technol Ther 15(10):836–844
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Zecchin, C., Facchinetti, A., Sparacino, G., Cobelli, C. (2015). Jump Neural Network for Real-Time Prediction of Glucose Concentration. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_15
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DOI: https://doi.org/10.1007/978-1-4939-2239-0_15
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