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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-...
| Autores principales: | , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Nature Publishing Group UK
2019
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| Materias: | |
| 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 |