Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial.

ABSTRACT: The personalized titration and optimization of insulin regimens for treatment of type 2 diabetes (T2D) are resource-demanding healthcare tasks. Here we propose a model-based reinforcement learning (RL) framework (called RL-DITR), which learns the optimal insulin regimen by analyzing glycemic state rewards through patient model interactions. When evaluated during the development phase for managing hospitalized patients with T2D, RL-DITR achieved superior insulin titration optimization (mean absolute error (MAE) of 1.10 ± 0.03 U) compared to other deep learning models and standard clinical methods. We performed a stepwise clinical validation of the artificial intelligence system from simulation to deployment, demonstrating better performance in glycemic control in inpatients compared to juni

SUBMITTER: Wang G 

PROVIDER: S-EPMC10579102 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature