血糖性
医学
低血糖
2型糖尿病
养生
临床试验
强化学习
胰岛素
糖尿病
内科学
机器学习
计算机科学
内分泌学
作者
Guangyu Wang,Xiaohong Liu,Zhen Ying,Guoxing Yang,Zhiwei Chen,Zhiwen Liu,Min Zhang,Hongmei Yan,Yuxing Lu,Yuanxu Gao,Kanmin Xue,Xiaoying Li,Chen Ying
出处
期刊:Nature Medicine
[Springer Nature]
日期:2023-09-14
卷期号:29 (10): 2633-2642
被引量:10
标识
DOI:10.1038/s41591-023-02552-9
摘要
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 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 .
科研通智能强力驱动
Strongly Powered by AbleSci AI