Insulin Regimen ML-based control for T2DM patients

TL;DR

This paper models blood glucose levels in T2DM patients as a stochastic process influenced by insulin and physiological factors, using reinforcement learning to optimize insulin treatment policies for better glucose control.

Contribution

It introduces a novel MDP-based framework employing model-based RL to personalize insulin regimens for T2DM patients, accounting for physiological variability.

Findings

Successful formulation of BGL as a Markov Decision Process

Demonstration of RL-based policy optimization for insulin control

Potential for personalized diabetes management

Abstract

\begin{abstract} We model individual T2DM patient blood glucose level (BGL) by stochastic process with discrete number of states mainly but not solely governed by medication regimen (e.g. insulin injections). BGL states change otherwise according to various physiological triggers which render a stochastic, statistically unknown, yet assumed to be quasi-stationary, nature of the process. In order to express incentive for being in desired healthy BGL we heuristically define a reward function which returns positive values for desirable BG levels and negative values for undesirable BG levels. The state space consists of sufficient number of states in order to allow for memoryless assumption. This, in turn, allows to formulate Markov Decision Process (MDP), with an objective to maximize the total reward, summarized over a long run. The probability law is found by model-based reinforcement learning (RL) and the optimal insulin treatment policy is retrieved from MDP solution.