Title: Long short-term memory-based model predictive control of blood glucose level for type 1 diabetes mellitus treatment

Authors: Nitesh Kumar Barnawal; Hoo Sang Ko; Sarah Park; H. Felix Lee; Guim Kwon

Addresses: Department of Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA ' Department of Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA ' University Library, University of Illinois Urbana-Champaign, Urbana, Illinois, USA ' Department of Industrial Engineering, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA ' Department of Pharmaceutical Sciences, Southern Illinois University Edwardsville, Edwardsville, Illinois, USA

Abstract: This paper presents a novel method to control blood glucose levels (BGL) based on predictions made by a long short-term memory (LSTM) network. An initial LSTM model was trained with data from rats with type 1 diabetes mellitus (T1DM) using Open Artificial Pancreas System (OpenAPS). Transfer learning was applied to develop an individualised prediction model based on the initial model. The LSTM model predicted BGL with a root mean squared error (RMSE) of 11.8240 mg/dl. The model was integrated into model predictive control (LSTM-MPC), which optimised insulin injection based on BGL predictions. Evaluated against a neural network-based MPC (NN-MPC) and OpenAPS using different diets and rats, LSTM-MPC outperformed both in control performance. This study demonstrated a closed-loop BGL control system tested with in vivo diabetic rats. The prediction model is re-trainable quickly using small datasets obtained from individual rats, which provides a feasible solution for individualised T1DM treatment.

Keywords: LSTM; blood glucose level prediction; type 1 diabetes mellitus (T1DM); model predictive control; transfer learning; time series forecasting; artificial pancreas system.

DOI: 10.1504/IJBRA.2025.146350

International Journal of Bioinformatics Research and Applications, 2025 Vol.21 No.3, pp.310 - 333

Received: 08 Nov 2023
Accepted: 18 Jun 2024
Published online: 23 May 2025 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article