J. Manuel Colmenar
ABSTRACT
Diabetes mellitus is a disease that affects more than three hundreds million people worldwide. Maintaining a good control of the disease is critical to avoid not only severe long-term complications but also dangerous short-term situations. Diabetics need to decide the appropriate insulin injection, thus they need to be able to estimate the level of glucose they are going to have after a meal. In this paper we use machine learning techniques for predicting glycemia in diabetic patients. The algorithms utilize data collected from real patients by a continuous glucose monitoring system, the estimated number of carbohydrates, and insulin administration for each meal. We compare (1) non-linear regression with fixed model structure, (2) identification of prognosis models by symbolic regression using genetic programming, (3) prognosis by k-nearest-neighbor time series search, and (4) identification of prediction models by grammatical evolution. We consider predictions horizons of 30, 60, 90 and 120 minutes.
- Michael Affenzeller, Stephan Winkler, Stefan Wagner, and Andreas Beham. Genetic Algorithms and Genetic Programming - Modern Concepts and Practical Applications. Chapman & Hall / CRC, 2009. Google Scholar
Digital Library
- Naomi S. Altman. An introduction to kernel and nearest-neighbor nonparametric regression. The American Statistician, 46(3):175--185, 1992.Google ScholarCross Ref
- Wolfgang Banzhaf and Christian W.G. Lasarczyk. Genetic programming of an algorithmic chemistry. In U. O'Reilly, T. Yu, R. Riolo, and B. Worzel, editors, Genetic Programming Theory and Practice II, pages 175--190. Ann Arbor, 2004.Google Scholar
- C. Cobelli, C. Dalla Man, G. Sparacino, L. Magni, G. De Nicolao, and B.P. Kovatchev. Diabetes: Models, signals, and control. Biomedical Engineering, IEEE Reviews in, 2:54 --96, 2009.Google Scholar
- International Diabetes Foundation. IDF Diabetes Atlas 2014, https://www.idf.org/sites/default/files/Atlas-poster-2014_EN.pdf.Google Scholar
- Nikolaus Hansen and Andreas Ostermeier. Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation, 9(2):159--195, June 2001. Google ScholarDigital Library
- Michael Kommenda, Gabriel Kronberger, Stefan Wagner, Stephan Winkler, and Michael Affenzeller. On the architecture and implementation of tree-based genetic programming in heuristiclab. In Proceedings of the 14th Annual Conference Companion on Genetic and Evolutionary Computation, GECCO '12, pages 101--108, New York, NY, USA, 2012. ACM. Google ScholarDigital Library
- John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. The MIT Press, 1992. Google ScholarDigital Library
- C.D. Man, R.A. Rizza, and C. Cobelli. Mixed meal simulation model of glucose-insulin system. In Engineering in Medicine and Biology Society, 2006. EMBS '06. 28th Annual International Conference of the IEEE, pages 307--310, 30 2006-Sept. 3.Google ScholarCross Ref
- Ingo Rechenberg. Evolutionsstrategie. Friedrich Frommann Verlag, 1973.Google Scholar
- Hans-Paul Schwefel. Evolutionsstrategie und numerische Optimierung. PhD thesis, Technische Universität Berlin, 1975.Google Scholar
- G. Sparacino, F. Zanderigo, S. Corazza, A. Maran, A. Facchinetti, and C. Cobelli. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. Biomedical Engineering, IEEE Transactions on, 54(5):931--937, may 2007.Google Scholar
- Stefan Wagner, Gabriel Kronberger, Andreas Beham, Michael Kommenda, Andreas Scheibenpflug, Erik Pitzer, Stefan Vonolfen, Monika Kofler, Stephan M. Winkler, Viktoria Dorfer, and Michael Affenzeller. Architecture and design of the heuristiclab optimization environment. Advanced Methods and Applications in Computational Intelligence, Topics in Intelligent Engineering and Informatics, 6:197--261, 2013.Google ScholarCross Ref
- Stephan M. Winkler. Evolutionary System Identification: Modern Concepts and Practical Applications. Schriften der Johannes Kepler Universitat Linz. Universitatsverlag Rudolf Trauner, 2009.Google Scholar
- David Zeevi and Tal Korem et al. Personalized nutrition by prediction of glycemic responses. Cell, 163(5):1079--1094, 2015.Google ScholarCross Ref
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- Predicting Glycemia in Diabetic Patients By Evolutionary Computation and Continuous Glucose Monitoring
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