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

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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.

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Authors and Affiliations

  1. Department of Mechanical and Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA

    Saeid Bahremand, Hoo Sang Ko, Ramin Balouchzadeh & H. Felix Lee

  2. Research and Instructional Services, Duke University, Durham, NC, 27708, USA

    Sarah Park

  3. Department of Pharmaceutical Sciences, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, USA

    Guim Kwon

Authors
  1. Saeid Bahremand
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  2. Hoo Sang Ko
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Correspondence to Hoo Sang Ko.

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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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Bahremand, S., Ko, H.S., Balouchzadeh, R. et al. Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system. Med Biol Eng Comput 57, 177–191 (2019). https://doi.org/10.1007/s11517-018-1872-6

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  • DOI: https://doi.org/10.1007/s11517-018-1872-6

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