Reinforcement Learning-Based Adaptive Insulin Advisor for Individuals with Type 1 Diabetes Patients under Multiple Daily Injections Therapy

2019/06/07 by Qingnan Sun, Sun, Qingnan, Marko V. Jankovic +3
Medicine · #Diabetes Management and Research #Diabetes Treatment and Management #FOS: Biological sciences #Pancreatic function and diabetes #Tissues and Organs (q-bio.TO)

paper · pdf · doi:10.48550/arxiv.1906.08586

openalex publication_date 2019/06/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The existing adaptive basal-bolus advisor (ABBA) was further developed to benefit patients under insulin therapy with multiple daily injections (MDI). Three different in silico experiments were conducted with the DMMS.R simulator to validate the approach of combined use of self-monitoring of blood glucose (SMBG) and insulin injection devices, e.g. insulin pen, as are used by the majority of type 1 diabetes patients under insulin therapy. The proposed approach outperforms the conventional method, as it increases the time spent within the target range and simultaneously reduces the risks of hyperglycaemic and hypoglycaemic events.

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