Abstract
In this paper, we provide an approach to data-driven control for artificial pancreas systems by learning neural network models of human insulin-glucose physiology from available patient data and using a mixed integer optimization approach to control blood glucose levels in real-time using the inferred models. First, our approach learns neural networks to predict the future blood glucose values from given data on insulin infusion and their resulting effects on blood glucose levels. However, to provide guarantees on the resulting model, we use quantile regression to fit multiple neural networks that predict upper and lower quantiles of the future blood glucose levels, in addition to the mean.
Using the inferred set of neural networks, we formulate a model-predictive control scheme that adjusts both basal and bolus insulin delivery to ensure that the risk of harmful hypoglycemia and hyperglycemia are bounded using the quantile models while the mean prediction stays as close as possible to the desired target. We discuss how this scheme can handle disturbances from large unannounced meals as well as infeasibilities that result from situations where the uncertainties in future glucose predictions are too high. We experimentally evaluate this approach on data obtained from a set of 17 patients over a course of 40 nights per patient. Furthermore, we also test our approach using neural networks obtained from virtual patient models available through the UVA-Padova simulator for type-1 diabetes.
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References
Abadi, M., et al.: Tensorflow: large-scale machine learning on heterogeneous distributed systems. CoRR abs/1603.04467 (2016). http://arxiv.org/abs/1603.04467
Atlas, E., Nimri, R., Miller, S., Grunberg, E.A., Phillip, M.: MD-logic artificial pancreas system: a pilot study in adults with type 1 diabetes. Diab. Care 33(5), 1072–1076 (2010)
Behl, M., Jain, A., Mangharam, R.: Data-driven modeling, control and tools for cyber-physical energy systems. In: Proceedings of the 7th International Conference on Cyber-Physical Systems, ICCPS 2016, pp. 35:1–35:10. IEEE Press, Piscataway (2016)
Bequette, B.W.: Algorithms for a closed-loop artificial pancreas: the case for model predictive control. J. Diab. Sci. Technol. 7, 1632–1643 (2013)
Bergman, R.N., Urquhart, J.: The pilot gland approach to the study of insulin secretory dynamics. Recent Progress Hormon. Res. 27, 583–605 (1971)
Bergman, R.N.: Minimal model: perspective from 2005. Hormon. Res. 64(suppl 3), 8–15 (2005)
Bhat, N., McAvoy, T.J.: Use of neural nets for dynamic modeling and control of chemical process systems. Comput. Chem. Eng. 14(4–5), 573–582 (1990)
Camacho, E., Bordons, C., Alba, C.: Model Predictive Control. Advanced Textbooks in Control and Signal Processing. Springer, London (2004). https://doi.org/10.1007/978-0-85729-398-5
Cameron, F., Niemeyer, G., Bequette, B.W.: Extended multiple model prediction with application to blood glucose regulation. J. Process Control 22(8), 1422–1432 (2012)
Cameron, F., et al.: Inpatient studies of a Kalman-filter-based predictive pump shutoff algorithm. J. Diab. Sci. Technol. 6(5), 1142–1147 (2012)
Chase, H.P., Maahs, D.: Understanding Diabetes (Pink Panther Book), 12 edn. Children’s Diabetes Foundation, Denver (2011). Available online through CU Denver Barbara Davis Center for Diabetes
Chee, F., Fernando, T.: Closed-Loop Control of Blood Glucose. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74031-5
Chen, X., Dutta, S., Sankaranarayanan, S.: Formal verification of a multi-basal insulin infusion control model. In: Workshop on Applied Verification of Hybrid Systems (ARCH), p. 16. Easychair (2017)
Cobelli, C., Dalla Man, C., Sparacino, G., Magni, L., Nicolao, G.D., Kovatchev, B.P.: Diabetes: models, signals and control (methodological review). IEEE Rev. Biomed. Eng. 2, 54–95 (2009)
Dalla Man, C., Camilleri, M., Cobelli, C.: A system model of oral glucose absorption: validation on gold standard data. IEEE Trans. Biomed. Eng. 53(12), 2472–2478 (2006)
Dalla Man, C., Micheletto, F., Lv, D., Breton, M., Kovatchev, B., Cobelli, C.: The UVa/Padova type I diabetes simulator: new features. J. Diab. Sci. Technol. 8(1), 26–34 (2014)
Dalla Man, C., Raimondo, D.M., Rizza, R.A., Cobelli, C.: Gim, simulation software of meal glucose-insulin model (2007)
Dalla Man, C., Rizza, R.A., Cobelli, C.: Meal simulation model of the glucose-insulin system. IEEE Trans. Biomed. Eng. 1(10), 1740–1749 (2006)
Dutta, S., Jha, S., Sankaranarayanan, S., Tiwari, A.: Output range analysis for deep feedforward neural networks. In: Dutle, A., Muñoz, C., Narkawicz, A. (eds.) NFM 2018. LNCS, vol. 10811, pp. 121–138. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-77935-5_9
Freeman, J.S.: Insulin analog therapy: improving the match with physiologic insulin secretion. J. Am. Osteopath. Assoc. 109(1), 26–36 (2009)
Garg, S.K., et al.: Glucose outcomes with the in-home use of a hybrid closed-loop insulin delivery system in adolescents and adults with type 1 diabetes. Diab. Technol. Ther. 19(3), 1–9 (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Griva, L., Breton, M., Chernavvsky, D., Basualdo, M.: Commissioning procedure for predictive control based on arx models of type 1 diabetes mellitus patients. IFAC-PapersOnLine 50(1), 11023–11028 (2017)
van Heusden, K., Dassau, E., Zisser, H.C., Seborg, D.E., Doyle III, F.J.: Control-relevant models for glucose control using a priori patient characteristics. IEEE Trans. Biomed. Eng. 59(7), 1839–1849 (2012)
Hakami, H.: FDA approves MINIMED 670G system - world’s first hybrid closed loop system (2016)
Hovorka, R., et al.: Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes. Physiol. Measur. 25, 905–920 (2004)
Hovorka, R., et al.: Partitioning glucose distribution/transport, disposal and endogenous production during IVGTT. Am. J. Physiol. Endocrinol. Metab. 282, 992–1007 (2002)
Hovorka, R.: Continuous glucose monitoring and closed-loop systems. Diab. Med. 23(1), 1–12 (2005)
Katz, G., Barrett, C., Dill, D.L., Julian, K., Kochenderfer, M.J.: Reluplex: an efficient SMT solver for verifying deep neural networks. In: Majumdar, R., Kunčak, V. (eds.) CAV 2017. LNCS, vol. 10426, pp. 97–117. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-63387-9_5
Koenker, R.: Quantile Regression. Econometric Society Monographs, no. 38, p. 342 (2005)
Kowalski, A.: Pathway to artificial pancreas revisited: moving downstream. Diab. Care 38, 1036–1043 (2015)
Kushner, T., Bortz, D., Maahs, D., Sankaranarayanan, S.: A data-driven approach to artificial pancreas verification and synthesis. In: International Conference on Cyber-Physical Systems (ICCPS 2018). IEEE Press (2018)
Lomuscio, A., Maganti, L.: An approach to reachability analysis for feed-forward relu neural networks. CoRR abs/1706.07351 (2017). http://arxiv.org/abs/1706.07351
Maahs, D.M., et al.: A randomized trial of a home system to reduce nocturnal hypoglycemia in type 1 diabetes. Diab. Care 37(7), 1885–1891 (2014)
Medtronic Inc.: “paradigm” insulin pump with low glucose suspend system (2012). cf. http://www.medtronicdiabetes.ca/en/paradigm_veo_glucose.html
Nimri, R., et al.: Night glucose control with md-logic artificial pancreas in home setting: a single blind, randomized crossover trial-interim analysis. Pediatric Diab. 15(2), 91–100 (2014)
Paoletti, N., Liu, K.S., Smolka, S.A., Lin, S.: Data-driven robust control for type 1 diabetes under meal and exercise uncertainties. In: Feret, J., Koeppl, H. (eds.) CMSB 2017. LNCS, vol. 10545, pp. 214–232. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-67471-1_13
Patek, S., et al.: In silico preclinical trials: methodology and engineering guide to closed-loop control in type 1 diabetes mellitus. J. Diab. Sci. Technol. 3(2), 269–82 (2009)
Pérez-Gandía, C., et al.: Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diab. Technol. Ther. 12(1), 81–88 (2010)
Piche, S., Sayyar-Rodsari, B., Johnson, D., Gerules, M.: Nonlinear model predictive control using neural networks. IEEE Control Syst. 20(3), 53–62 (2000)
Psichogios, D.C., Ungar, L.H.: Direct and indirect model based control using artificial neural networks. Indus. Eng. Chem. Res. 30(12), 2564–2573 (1991)
Ruiz, J.L., et al.: Effect of insulin feedback on closed-loop glucose control: a crossover study. J. Diab. Sci. Technol. 6(5), 1123–1130 (2012)
Steil, G.M., Rebrin, K., Darwin, C., Hariri, F., Saad, M.F.: Feasibility of automating insulin delivery for the treatment of type 1 diabetes. Diabetes 55, 3344–3350 (2006)
Teixeira, R.E., Malin, S.: The next generation of artificial pancreas control algorithms. J. Diabetes Sci. Tech. 2, 105–112 (2008)
Vanderbei, R.J.: Linear Programming: Foundations & Extensions, Second Edn. Springer, Heidelberg (2001). https://doi.org/10.1007/978-1-4614-7630-6, cf. http://www.princeton.edu/~rvdb/LPbook/
Visentin, R., Dalla Man, C., Cobelli, C.: One-day Bayesian cloning of type 1 diabetes subjects: toward a single-day UVa/Padova type 1 diabetes simulator. IEEE Trans. Biomed. Eng. 63(11), 2416–2424 (2016)
Wang, T., Gao, H., Qiu, J.: A combined adaptive neural network and nonlinear model predictive control for multirate networked industrial process control. IEEE Trans. Neural Netw. Learn. Syst. 27(2), 416–425 (2016)
Weinzimer, S., Steil, G., Swan, K., Dziura, J., Kurtz, N., Tamborlane, W.: Fully automated closed-loop insulin delivery versus semiautomated hybrid control in pediatric patients with type 1 diabetes using an artificial pancreas. Diab. Care 31, 934–939 (2008)
Acknowledgments
This work was supported by the US National Science Foundation (NSF) through awards 1446900, 1646556, and 1815983. All opinions expressed are those of the authors and not necessarily of the NSF.
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Dutta, S., Kushner, T., Sankaranarayanan, S. (2018). Robust Data-Driven Control of Artificial Pancreas Systems Using Neural Networks. In: Češka, M., Šafránek, D. (eds) Computational Methods in Systems Biology. CMSB 2018. Lecture Notes in Computer Science(), vol 11095. Springer, Cham. https://doi.org/10.1007/978-3-319-99429-1_11
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