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

"for the reproduction of our code and model we have also deposited a minimum dataset at zenodo ( https://zenodo org/record/8198049 ) which is publicly available for scientific research and non-commercial use."

"the codes are available for scientific research and non-commercial use on github at https://github com/rlditr23/rl-ditr ."

"Competing interests The authors declare no competing interests."

"This study was funded by the National Natural Science Foundation of China (grants 81820108008 and 31830041 to X. Li, grant 62272055 to G.W. and grant 82000822 to Y.C.); the Young Elite Scientists Sponsorship Program by CAST (grant 2021QNRC001 to G.W.); and the Shanghai Municipal Health Commission (grant 2022JC015 to X. Li). G.W. is also supported by the New Cornerstone Science Foundation through the XPLORER PRIZE. K.X. is supported by the Wellcome Trust and National Institute for Health and Care Research Oxford Biomedical Research Centre. The funders of the study had no role in study design, data collection, data analysis, data interpretation or writing of the report. The authors would like to acknowledge the Nanjing Institute of InforSuperBahn MLOps for providing the training and evaluation platform."

"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 junior and intermediate-level physicians through quantitative (MAE of 1.18 ± 0.09 U) and qualitative metrics from a blinded review. Additionally, we conducted a single-arm, patient-blinded, proof-of-concept feasibility trial in 16 patients with T2D. The primary outcome was difference in mean daily capillary blood glucose during the trial, which decreased from 11.1 (±3.6) to 8.6 (±2.4) mmol L−1 (P < 0.01), meeting the pre-specified endpoint. No episodes of severe hypoglycemia or hyperglycemia with ketosis occurred. These preliminary results warrant further investigation in larger, more diverse clinical studies. ClinicalTrials.gov registration: NCT05409391."