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

Annu Int Conf IEEE Eng Med Biol Soc. 2019 Jul;2019:3609-3612. doi: 10.1109/EMBC.2019.8857178.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Blood Glucose Self-Monitoring
  • Computer Simulation
  • Diabetes Mellitus, Type 1 / drug therapy*
  • Humans
  • Hypoglycemic Agents / administration & dosage*
  • Insulin / administration & dosage*
  • Insulin Infusion Systems
  • Reinforcement, Psychology*

Substances

  • Hypoglycemic Agents
  • Insulin