4.6 Article

Toward a Fully Automated Artificial Pancreas System Using a Bioinspired Reinforcement Learning Design: In Silico Validation

Journal

IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
Volume 25, Issue 2, Pages 536-546

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2020.3002022

Keywords

Artificial pancreas; artificial intelligent; automated insulin treatment; diabetes; reinforcement learning

Funding

  1. ICT Consilience Creative program [IITP-2019-2011-1-00783]
  2. National Research Founcdation of Korea (NRF) - Korea government (MSIT) [NRF-2017R1A5A1015596, 2020R1A2C2005385]
  3. POSCO Green Science Project - POSCO (Pohang Iron & Steel Co. Ltd), Korea
  4. National Research Foundation of Korea [2020R1A2C2005385] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)

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The article introduces a novel reinforcement learning based artificial intelligence algorithm for automated insulin infusion control. Through training on virtual patients in the FDA approved UVA/Padova simulator, the algorithm achieves fully automated blood glucose control.
Objective: The automation of insulin treatment is the most challenge aspect of glucose management for type 1 diabetes owing to unexpected exogenous events (e.g., meal intake). In this article, we propose a novel reinforcement learning (RL) based artificial intelligence (AI) algorithm for a fully automated artificial pancreas (AP) system. Methods: A bioinspired RL designing method was developed for automated insulin infusion. This method has reward functions that imply the temporal homeostatic objective and discount factors that reflect an individual specific pharmacological characteristic. The proposed method was applied to a training method using an RL algorithm and was evaluated in virtual patients from the FDA approved UVA/Padova simulator with unannounced meal intakes. Results: For a single-meal experiment with preprandial fasting, the trained policy demonstrated fully automated regulation in both the basal and postprandial phases. In the in silico trial with a variation of insulin sensitivity and dawn phenomenon, the policy achieved a mean glucose of 124.72 mgAL and percentage time in the normal range of 89.56%. The layer-wise relevance propagation provides interpretable information on AI-driven decision for robustness to sensor noise, automated postprandial regulation, and insulin stacking avoidance. Conclusion: The AP algorithm based on the bioinspired RL approach enables fully automated blood glucose control with unannounced meal intake. Significance: The proposed framework can be extended to other drug-based treatments for systems with significant uncertainties.

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