People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to 65.9% with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An >In Silico Validation


    Beteiligte:
    Zhu, T (Autor:in) / Li, K (Autor:in) / Herrero, P (Autor:in) / Georgiou, P (Autor:in)

    Erscheinungsdatum :

    2021-04-01


    Anmerkungen:

    IEEE Journal of Biomedical and Health Informatics , 25 (4) pp. 1223-1232. (2021)


    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629





    An Insulin Bolus Advisor for Type 1 Diabetes Using Deep Reinforcement Learning

    Zhu, T / Li, K / Kuang, L et al. | BASE | 2020

    Freier Zugriff

    Modular production control using deep reinforcement learning

    Mayer, Sebastian / Classen, Tobias / Endisch, Christian | BASE | 2022

    Freier Zugriff

    Intelligent Formation Control using Deep Reinforcement Learning

    Johns, Rasmus Johns | BASE | 2018

    Freier Zugriff