scholarly journals Nonlinear Physiological Model of Insulin-Glucose Regulation System in Type 1 Diabetes Mellitus

2019 ◽  
Vol 15 (2) ◽  
pp. 78-88
Author(s):  
Ahmed Ali ◽  
Fadhil Tahir
Keyword(s):  
Mathematical Modeling ◽  
Diabetes Mellitus ◽  
Mathematical Model ◽  
Type 1 Diabetes ◽  
Common Property ◽  
Physiological Model ◽  
Volterra Model ◽  
Glucose Regulation ◽  
Regulation System

Mathematical modeling is very effective method to investigate interaction between insulin and glucose. In this paper, a new mathematical model for insulin-glucose regulation system is introduced based on well-known Lokta-Volterra model. Chaos is a common property in complex biological systems in the previous studies. The results here are in accordance with previous ones and indicating that insulin-glucose regulating system has many dynamics in different situations. The overall result of this paper may be helpful for better understanding of diabetes mellitus regulation system including diseases such as hyperinsulinemia and Type1 DM.

scholarly journals In-Silico Evaluation of Glucose Regulation Using Policy Gradient Reinforcement Learning for Patients with Type 1 Diabetes Mellitus

2020 ◽  
Vol 10 (18) ◽  
pp. 6350
Author(s):  
Jonas Nordhaug Myhre ◽  
Miguel Tejedor ◽  
Ilkka Kalervo Launonen ◽  
Anas El Fathi ◽  
Fred Godtliebsen
Keyword(s):  
Diabetes Mellitus ◽  
Type 1 Diabetes ◽  
Reinforcement Learning ◽  
Blood Glucose ◽  
State Of The Art ◽  
Blood Glucose Control ◽  
Glucose Regulation ◽  
Gradient Algorithms ◽  
Policy Gradient

In this paper, we test and evaluate policy gradient reinforcement learning for automated blood glucose control in patients with Type 1 Diabetes Mellitus. Recent research has shown that reinforcement learning is a promising approach to accommodate the need for individualized blood glucose level control algorithms. The motivation for using policy gradient algorithms comes from the fact that adaptively administering insulin is an inherently continuous task. Policy gradient algorithms are known to be superior in continuous high-dimensional control tasks. Previously, most of the approaches for automated blood glucose control using reinforcement learning has used a finite set of actions. We use the Trust-Region Policy Optimization algorithm in this work. It represents the state of the art for deep policy gradient algorithms. The experiments are carried out in-silico using the Hovorka model, and stochastic behavior is modeled through simulated carbohydrate counting errors to illustrate the full potential of the framework. Furthermore, we use a model-free approach where no prior information about the patient is given to the algorithm. Our experiments show that the reinforcement learning agent is able to compete with and sometimes outperform state-of-the-art model predictive control in blood glucose regulation.

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