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

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 r...

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Detalles Bibliográficos
Autores principales: Wang, Guangyu, Liu, Xiaohong, Ying, Zhen, Yang, Guoxing, Chen, Zhiwei, Liu, Zhiwen, Zhang, Min, Yan, Hongmei, Lu, Yuxing, Gao, Yuanxu, Xue, Kanmin, Li, Xiaoying, Chen, Ying
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group US 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579102/
https://www.ncbi.nlm.nih.gov/pubmed/37710000
http://dx.doi.org/10.1038/s41591-023-02552-9

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10579102/
https://www.ncbi.nlm.nih.gov/pubmed/37710000
http://dx.doi.org/10.1038/s41591-023-02552-9

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