Auto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization

TítuloAuto adaptation of closed-loop insulin delivery system using continuous reward functions and incremental discretization
Tipo de PublicaciónJournal Article
Año de Publicación2023
AutoresSerafini MCecilia, Rosales N, Garelli F
JournalComputer Methods in Biomechanics and Biomedical Engineering
Páginas1-12
Resumen

Several closed or hybrid loop controllers for Blood Glucose (BG) regulation, which are also known as Artificial Pancreas (AP) Systems or Automated Insulin Delivery systems (AIDs), are in development worldwide. Most AIDs are designed and evaluated for short-term performance, with a particular emphasis on the post-meal period. However, if controllers are not adapted properly to account for variations in physiology that affect Insulin Sensitivity (IS), the AIDs may perform inadequately. In this work, the performance of two Reinforcement Learning (RL) agents trained under both piecewise and continuous reward functions is evaluated in-silico for long-term adaptation of a Fully Automated Insulin Delivery (fAID) system. An automatic adaptive discretization scheme that expands the state space as needed is also implemented to avoid disproportionate state space exploration. The proposed agents are evaluated for long-term adaptation of the Automatic Regulation of Glucose (ARG) algorithm, considering variations in IS. Results show that both RL agents have improved performance compared to a rule-based decision-making approach and the baseline controller for the majority of the adult population. Moreover, the use of a continuous shaped reward function proves to enhance the performance of the agents further than a piecewise one.

URLhttps://doi.org/10.1080/10255842.2023.2241945
DOI10.1080/10255842.2023.2241945
Líneas de investigación: 
Control de sistemas y procesos biológicos
Control of biological processes and systems