Can clustering improve glucose forecasting with genetic programming models?
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José Ignacio Hidalgo | Oscar Garnica | Juan Lanchares | Sergio Contador | J. Manuel Velasco | J. Lanchares | O. Garnica | J. Hidalgo | J. M. Velasco | Sergio Contador
[1] L. Magni,et al. Multinational Study of Subcutaneous Model-Predictive Closed-Loop Control in Type 1 Diabetes Mellitus: Summary of the Results , 2010, Journal of diabetes science and technology.
[2] G. V. Kass. An Exploratory Technique for Investigating Large Quantities of Categorical Data , 1980 .
[3] Janet M. Allen,et al. Manual closed-loop insulin delivery in children and adolescents with type 1 diabetes: a phase 2 randomised crossover trial , 2010, The Lancet.
[4] Clifford M. Hurvich,et al. Regression and time series model selection in small samples , 1989 .
[5] D. Cox,et al. Evaluating Clinical Accuracy of Systems for Self-Monitoring of Blood Glucose , 1987, Diabetes Care.
[6] J. Lachin,et al. Intensive Diabetes Treatment and Cardiovascular Outcomes in Type 1 Diabetes: The DCCT/EDIC Study 30-Year Follow-up , 2016, Diabetes Care.
[7] George Papadakis,et al. Comparative analysis of a-priori and a-posteriori dietary patterns using state-of-the-art classification algorithms: A case/case-control study , 2013, Artif. Intell. Medicine.
[8] John R. Koza,et al. Genetic programming - on the programming of computers by means of natural selection , 1993, Complex adaptive systems.
[9] Ivanoe De Falco,et al. Genetic Programming-based induction of a glucose-dynamics model for telemedicine , 2018, J. Netw. Comput. Appl..
[10] José Ignacio Hidalgo,et al. glUCModel: A monitoring and modeling system for chronic diseases applied to diabetes , 2014, J. Biomed. Informatics.
[11] Giuseppe De Nicolao,et al. Model individualization for artificial pancreas , 2016, Comput. Methods Programs Biomed..
[12] B H Ginsberg,et al. A new consensus error grid to evaluate the clinical significance of inaccuracies in the measurement of blood glucose. , 2000, Diabetes care.
[13] José Ignacio Hidalgo,et al. A genetic algorithm approach to customizing a glucose model based on usual therapeutic parameters , 2017, Progress in Artificial Intelligence.
[14] Sean Luke,et al. Two fast tree-creation algorithms for genetic programming , 2000, IEEE Trans. Evol. Comput..
[15] Michael Affenzeller,et al. HeuristicLab: A Generic and Extensible Optimization Environment , 2005 .
[16] Maarten Keijzer,et al. Improving Symbolic Regression with Interval Arithmetic and Linear Scaling , 2003, EuroGP.
[17] F. Hariri,et al. Interstitial fluid glucose dynamics during insulin-induced hypoglycaemia , 2005, Diabetologia.
[18] Marco Forgione,et al. Run-to-Run Tuning of Model Predictive Control for Type 1 Diabetes Subjects: In Silico Trial , 2009, Journal of diabetes science and technology.
[19] Lucy Mays,et al. Diabetes Mellitus Standards of Care. , 2015, The Nursing clinics of North America.
[20] David C Klonoff,et al. Technical Aspects of the Parkes Error Grid , 2013, Journal of diabetes science and technology.
[21] Ya-Xiong Tao,et al. Glucose homeostatis and the pathogenesis of diabetes mellitus , 2014 .
[22] J. Salonen,et al. Perceived health status and morbidity and mortality: evidence from the Kuopio ischaemic heart disease risk factor study. , 1996, International journal of epidemiology.
[23] H. Akaike,et al. Information Theory and an Extension of the Maximum Likelihood Principle , 1973 .
[24] Robert G. Sutherlin,et al. A Bihormonal Closed-Loop Artificial Pancreas for Type 1 Diabetes , 2010, Science Translational Medicine.
[25] José Ignacio Hidalgo,et al. Combining data augmentation, EDAs and grammatical evolution for blood glucose forecasting , 2018, Memetic Comput..
[26] Denis Gillet,et al. Preclinically assessed optimal control of postprandial glucose excursions for type 1 patients with diabetes , 2011, 2011 IEEE International Conference on Automation Science and Engineering.