Learning the Dynamic Treatment Regimes from Medical Registry Data through Deep Q-network

This paper presents the deep reinforcement learning (DRL) framework to estimate the optimal Dynamic Treatment Regimes from observational medical data. This framework is more flexible and adaptive for high dimensional action and state spaces than existing reinforcement learning methods to model real-...

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Detalles Bibliográficos
Autores principales: Liu, Ning, Liu, Ying, Logan, Brent, Xu, Zhiyuan, Tang, Jian, Wang, Yanzhi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365640/
https://www.ncbi.nlm.nih.gov/pubmed/30728403
http://dx.doi.org/10.1038/s41598-018-37142-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6365640/
https://www.ncbi.nlm.nih.gov/pubmed/30728403
http://dx.doi.org/10.1038/s41598-018-37142-0

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