Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods

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@Article{Hidalgo2017b,
  author =       "J. Ignacio Hidalgo and J. Manuel Colmenar and 
                 Gabriel Kronberger and Stephan M. Winkler and Oscar Garnica and 
                 Juan Lanchares",
  title =        "Data Based Prediction of Blood Glucose Concentrations
                 Using Evolutionary Methods",
  journal =      "Journal of Medical Systems",
  year =         "2017",
  volume =       "41",
  number =       "9",
  pages =        "142",
  month =        sep,
  note =         "Special issue on Patient Facing Systems",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Diabetes, Glucose prediction, Continuous
                 glucose monitoring, Evolutionary computation",
  ISSN =         "1573-689X",
  DOI =          "doi:10.1007/s10916-017-0788-2",
  size =         "20 pages",
  abstract =     "Predicting glucose values on the basis of insulin and
                 food intakes is a difficult task that people with
                 diabetes need to do daily. This is necessary as it is
                 important to maintain glucose levels at appropriate
                 values to avoid not only short-term, but also long-term
                 complications of the illness. Artificial intelligence
                 in general and machine learning techniques in
                 particular have already lead to promising results in
                 modelling and predicting glucose concentrations. In
                 this work, several machine learning techniques are used
                 for the modeling and prediction of glucose
                 concentrations using as inputs the values measured by a
                 continuous monitoring glucose system as well as also
                 previous and estimated future carbohydrate intakes and
                 insulin injections. In particular, we use the following
                 four techniques: genetic programming, random forests,
                 k-nearest neighbours, and grammatical evolution. We
                 propose two new enhanced modeling algorithms for
                 glucose prediction, namely (i) a variant of grammatical
                 evolution which uses an optimized grammar, and (ii) a
                 variant of tree-based genetic programming which uses a
                 three-compartment model for carbohydrate and insulin
                 dynamics. The predictors were trained and tested using
                 data of ten patients from a public hospital in Spain.
                 We analyse our experimental results using the Clarke
                 error grid metric and see that 90percent of the
                 forecasts are correct (i.e., Clarke error categories A
                 and B), but still even the best methods produce 5 to
                 10percent of serious errors (category D) and
                 approximately 0.5percent of very serious errors
                 (category E). We also propose an enhanced genetic
                 programming algorithm that incorporates a
                 three-compartment model into symbolic regression models
                 to create smoothed time series of the original
                 carbohydrate and insulin time series.",
}

Genetic Programming entries for Jose Ignacio Hidalgo Perez J Manuel Colmenar Gabriel Kronberger Stephan M Winkler Oscar Garnica J Lanchares

Citations