Abstract
The present work presents a comparative assessment of glucose prediction models for diabetic patients using data from sensors monitoring blood glucose concentration as well as data from in silico simulations. The models are based on neural networks and linear and nonlinear mathematical models evaluated for prediction horizons ranging from 5 to 120 min. Furthermore, the implementation of compartment models for simulation of absorption and elimination of insulin, caloric intake and information about physical activity is examined in combination with neural networks and mathematical models, respectively. This assessment also addresses the recent progress and challenges in designing glucose regulators based on model predictive control used as part of artificial pancreas devices for type 1 diabetic patients. The assessments include 24 papers in total, from 2006 to 2016, in order to investigate progress in blood glucose concentration prediction and in Artificial Pancreas devices for type 1 diabetic patients.
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References
Cobelli, C., Renard, E., Kovatchev, B.: Artificial pancreas: past, present, future. Diabetes 60, 2672–2682 (2011)
Clarke, W.L., Anderson, S., Farhy, L., Breton, M., Gonder-Frederick, L., Daniel, C., Boris, K.: Evaluating the clinical accuracy of two continuous glucose sensors using continuous glucose-error grid analysis. Diabetes Care 28(10), 2412–2417 (2005)
El-Khatib, F.H., Jiang, J., Damiano, E.R.: Adaptive closed-loop control provides blood-glucose regulation using dual subcutaneous insulin and glucagon infusion in diabetic swine. J. Diabetes Sci. Technol. 1(2), 181–192 (2007)
Eljil, K.S., Qadah, G., Pasquier, M.: Predicting hypoglycemia in diabetic patients using time - sensitive artificial neural network. Int. J. Healthcare Inf. Syst. Inform. 11(4), 70–88 (2016)
Favero, S.: First use of model predictive control in outpatient wearable artificial pancreas. Diabetes Care 37, 1212–1215 (2014)
Finan, D.A., Palerm, C.C., Doyle, F.J., Seborg, D.E.: Effect of input excitation on the quality of empirical dynamic models for type 1 diabetes. Am. Inst. Chem. Eng. 55(5), 1135–1146 (2009)
Gani, A., Gribok, A.V., Rajaraman, S., Ward, W.K., Reifman, J.: Predicting subcutaneous glucose concentration in humans: data-driven glucose modeling. IEEE Trans. Biomed. Eng. 56(2), 246–254 (2009)
Georga, E., Protopappas, V., Ardigò, D., Marina, M., Zavaroni, I., Polyzos, D., Fotiadis, D.I.: Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression. IEEE J. Biomed. Health Inform. 17(1), 71–81 (2013)
Georga, E., Protopappas, V., Fotiadis, D.I.: Glucose prediction in type 1 and type 2 diabetic patients using data driven techniques. In: INTECH (2011)
Hovorka, R., Allen, J.M., Elleri, D., Chassin, L.J., Harris, J., Xing, D., Kollman, C., Hovorka, T., Larsen, A.M.F., Nodale, M., De Palma, A.: Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial. Lancet 375, 743–751 (2010)
Lee, H., Bequette, B.W.: A closed-loop artificial pancreas based on model predictive control: Human-family identification and automatic meal disturbance rejection. Biomed. Sig. Process. Control 4, 347–354 (2009)
Magni, L., Raimondo, D.M., Man, C.D., Nicolao, G.D., Kovatchev, B., Cobelli, C.: Model predictive control of glucose concentration in subjects with type 1 diabetes: an in silico trial. In: IFAC (2008)
Markakis, M.G., Mitsis, G.D., Papavassilopoulos, G.P., Marmarelis, V.Z.: Model predictive control of blood glucose in type 1 diabetes: the principal dynamic modes approach. In: International IEEE EMBS Conference (2008)
Mougiakakou, S., Prountzou, A., Iliopoulou, D., Nikita, K.S., Vazeou, A., Bartsocas, C.S.: Neural network based glucose - insulin metabolism models for children with type 1 diabetes. In: Proceedings of the 28th IEEE, EMBS Annual International Conference New York City, USA, 30 August–3 September 2006
Oruklu, M.E., Cinar, A., Rollins, D.K., Quinn, L.: Adaptive system identification for estimating future glucose concentrations and hypoglycemia alarms. Automatica 48(8), 1892–1897 (2012). Elsevier
Pappada, S.M., Cameron, B., Rosman, P.M.: Development of a neural network for prediction of glucose concentration in type 1 diabetes patients. J. Diabetes Sci. Technol. 2(5), 792–801 (2008)
Quchani, S.A., Tahami, E.: Comparison of MLP and Elman neural network for blood glucose level prediction in type 1 diabetics. In: Ibrahim, F., Osman, N.A.A., Usman, J., Kadri, N.A. (eds.) 3rd Kuala Lumpur International Conference on Biomedical Engineering 2006. IFMBE, vol. 15. Springer, Heidelberg (2007)
Seron, M., Medioli, A., Goodwin, G.: A methodology for the comparison of traditional mpc and stohastic mpc in the context of the regulation of blood glucose levels in type 1 diabetes. In: Australian Control Conference (2016)
Soru, P., De Nicolao, G., Toffanin, C., Dalla Man, C., Cobelli, C., Magni, L., AP@ home consortium.: MPC based artificial pancreas: strategies for individualization and meal compensation. Ann. Rev. Control, 36(1), 118–128 (2012)
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. 54(5), 931–937 (2007)
Ståhl, F., Johansson, R.: Diabetes mellitus modeling and short-term prediction based on blood glucose measurements. Math. Biosci. 217(2), 101–117 (2009)
Valletta, J.J., Chipperfield, A.J., Byrne, C.D.: Gaussian process modelling of blood glucose response to free-living physical activity data in people with type 1 diabetes. In: 31st Annual International Conference of the IEEE EMBS (2009)
Zainuddin, Z., Pauline, O., Ardil, C.: A neural network apporach in predicting blood glucose level for diabetic patients. Inte. J. Comput. Electr. Autom. Control Inf. Eng. 2(3) (2009)
Zarkogianni, K., Mitsis, K., Litsa, E., Arredondo, M., Fico, G., Fioravanti, A., Nikita, K.: Comparative assessment of glucose prediction models for patients with type 1 diabetes mellitus applying sensors for glucose and physical activity. Int. Fed. Med. Biol. Eng. 53(12), 1333–1343 (2015)
Zhao, C., Dassau, E., Jovanovic, L., Zisser, H.C., Doyle, F., Seborg, D.E.: Predicting subcutaneous glucose concentration using a latent-variable-based statistical method for type 1 diabetes mellitus. J. Diabetes Sci. Technol. 6(3), 617–633 (2012)
Acknowledgements
Research has been supported by the AZV MZ CR project [No. 15-25710A] “Individual dynamics of glycaemia excursions identification in diabetic patients to improve self managing procedures influencing insulin dosage” and by CVUT institutional resources (SGS grant application [No. OHK-4-/3T/37]).
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Saiti, K., Macaš, M., Štechová, K., Pithová, P., Lhotská, L. (2017). A Review of Model Prediction in Diabetes and of Designing Glucose Regulators Based on Model Predictive Control for the Artificial Pancreas. In: Bursa, M., Holzinger, A., Renda, M., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2017. Lecture Notes in Computer Science(), vol 10443. Springer, Cham. https://doi.org/10.1007/978-3-319-64265-9_6
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