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Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system

  • Saeid Bahremand
  • Hoo Sang Ko
  • Ramin Balouchzadeh
  • H. Felix Lee
  • Sarah Park
  • Guim Kwon
Original Article
  • 71 Downloads

Abstract

Artificial pancreas system (APS) is a viable option to treat diabetic patients. Researchers, however, have not conclusively determined the best control method for APS. Due to intra-/inter-variability of insulin absorption and action, an individualized algorithm is required to control blood glucose level (BGL) for each patient. To this end, we developed model predictive control (MPC) based on artificial neural networks (ANNs), which combines ANN for BGL prediction based on inputs and MPC for BGL control based on the ANN (NN-MPC). First, we developed a mathematical model for diabetic rats, which was used to identify individual virtual subjects by fitting to empirical data collected through an APS, including BGL data, insulin injection, and food intake. Then, the virtual subjects were used to generate datasets for training ANNs. The NN-MPC determines control actions (insulin injection) based on BGL predicted by the ANN. To evaluate the NN-MPC, we conducted experiments using four virtual subjects under three different scenarios. Overall, the NN-MPC maintained BGL within the normal range about 90% of the time with a mean absolute deviation of 4.7 mg/dl from a desired BGL. Our findings suggest that the NN-MPC can provide subject-specific BGL control in conjunction with a closed-loop APS.

Graphical abstract

Keywords

Artificial neural network Artificial pancreas system Blood glucose level control Model predictive control Type 1 diabetes mellitus 

Notes

Compliance with ethical standards

All animal maintenance and treatment protocols complied with the Guide for Care and Use of Laboratory Animals as adopted by the National Institute of Health and approved by the Southern Illinois University Edwardsville (SIUE) Institutional Animal Care and Use Committee (IACUC).

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Department of Mechanical and Industrial EngineeringSouthern Illinois University EdwardsvilleEdwardsvilleUSA
  2. 2.Research and Instructional ServicesDuke UniversityDurhamUSA
  3. 3.Department of Pharmaceutical SciencesSouthern Illinois University EdwardsvilleEdwardsvilleUSA

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