Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events

Created by W.Langdon from gp-bibliography.bib Revision:1.3973

@InProceedings{Botwey:2014:EMBC,
  author =       "R. H. Botwey and E. Daskalaki and P. Diem and 
                 S. G. Mougiakakou",
  booktitle =    "36th Annual International Conference of the IEEE
                 Engineering in Medicine and Biology Society (EMBC
                 2014)",
  title =        "Multi-model data fusion to improve an early warning
                 system for hypo-/hyperglycemic events",
  year =         "2014",
  month =        aug,
  pages =        "4843--4846",
  abstract =     "Correct predictions of future blood glucose levels in
                 individuals with Type 1 Diabetes (T1D) can be used to
                 provide early warning of upcoming hypo-/hyperglycemic
                 events and thus to improve the patient's safety. To
                 increase prediction accuracy and efficiency, various
                 approaches have been proposed which combine multiple
                 predictors to produce superior results compared to
                 single predictors. Three methods for model fusion are
                 presented and comparatively assessed. Data from 23 T1D
                 subjects under sensor-augmented pump (SAP) therapy were
                 used in two adaptive data-driven models (an
                 autoregressive model with output correction - cARX, and
                 a recurrent neural network - RNN). Data fusion
                 techniques based on i) Dempster-Shafer Evidential
                 Theory (DST), ii) Genetic Algorithms (GA), and iii)
                 Genetic Programming (GP) were used to merge the
                 complimentary performances of the prediction models.
                 The fused output is used in a warning algorithm to
                 issue alarms of upcoming hypo-/hyperglycemic events.
                 The fusion schemes showed improved performance with
                 lower root mean square errors, lower time lags, and
                 higher correlation. In the warning algorithm, median
                 daily false alarms (DFA) of 0.25percent, and 100percent
                 correct alarms (CA) were obtained for both event types.
                 The detection times (DT) before occurrence of events
                 were 13.0 and 12.1 min respectively for
                 hypo-/hyperglycemic events. Compared to the cARX and
                 RNN models, and a linear fusion of the two, the
                 proposed fusion schemes represents a significant
                 improvement.",
  keywords =     "genetic algorithms, genetic programming",
  DOI =          "doi:10.1109/EMBC.2014.6944708",
  ISSN =         "1557-170X",
  notes =        "Also known as \cite{6944708}",
}

Genetic Programming entries for R H Botwey E Daskalaki P Diem S G Mougiakakou

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