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Jump Neural Network for Real-Time Prediction of Glucose Concentration

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Artificial Neural Networks

Part of the book series: Methods in Molecular Biology ((MIMB,volume 1260))

Abstract

Prediction of the future value of a variable is of central importance in a wide variety of fields, including economy and finance, meteorology, informatics, and, last but not least important, medicine. For example, in the therapy of Type 1 Diabetes (T1D), in which, for patient safety, glucose concentration in the blood should be maintained in a defined normoglycemic range, the ability to forecast glucose concentration in the short-term (with a prediction horizon of around 30 min) might be sufficient to reduce the incidence of hypoglycemic and hyperglycemic events. Neural Network (NN) approaches are suitable for prediction purposes because of their ability to model nonlinear dynamics and handle in their inputs signals coming from different domains. In this chapter we illustrate the design of a jump NN glucose prediction algorithm that exploits past glucose concentration data, measured in real-time by a minimally invasive continuous glucose monitoring (CGM) sensor, and information on ingested carbohydrates, supplied by the patient himself or herself. The methodology is assessed by tuning the NN on data of ten T1D individuals and then testing it on a dataset of ten different subjects. Results with a prediction horizon of 30 min show that prediction of glucose concentration in T1D via NN is feasible and sufficiently accurate. The average time anticipation obtained is compatible with the generation of preventive hypoglycemic and hyperglycemic alerts and the improvement of artificial pancreas performance.

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Authors and Affiliations

  1. Department of Information Engineering, University of Padova, Padova, Italy

    Chiara Zecchin, Andrea Facchinetti, Giovanni Sparacino & Claudio Cobelli

Authors
  1. Chiara Zecchin
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  2. Andrea Facchinetti
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  3. Giovanni Sparacino
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  4. Claudio Cobelli
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Corresponding author

Correspondence to Claudio Cobelli .

Editor information

Editors and Affiliations

  1. Chemistry Department, Oxford University, UK, and University of Victoria, Victoria, British Columbia, Canada

    Hugh Cartwright

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Zecchin, C., Facchinetti, A., Sparacino, G., Cobelli, C. (2015). Jump Neural Network for Real-Time Prediction of Glucose Concentration. In: Cartwright, H. (eds) Artificial Neural Networks. Methods in Molecular Biology, vol 1260. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-2239-0_15

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  • DOI: https://doi.org/10.1007/978-1-4939-2239-0_15

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4939-2238-3

  • Online ISBN: 978-1-4939-2239-0

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