Development and validation of a reinforcement learning algorithm to dynamically optimize mechanical ventilation in critical care

The aim of this work was to develop and evaluate the reinforcement learning algorithm VentAI, which is able to suggest a dynamically optimized mechanical ventilation regime for critically-ill patients. We built, validated and tested its performance on 11,943 events of volume-controlled mechanical ve...

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Detalles Bibliográficos
Autores principales: Peine, Arne, Hallawa, Ahmed, Bickenbach, Johannes, Dartmann, Guido, Fazlic, Lejla Begic, Schmeink, Anke, Ascheid, Gerd, Thiemermann, Christoph, Schuppert, Andreas, Kindle, Ryan, Celi, Leo, Marx, Gernot, Martin, Lukas
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895944/
https://www.ncbi.nlm.nih.gov/pubmed/33608661
http://dx.doi.org/10.1038/s41746-021-00388-6

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7895944/
https://www.ncbi.nlm.nih.gov/pubmed/33608661
http://dx.doi.org/10.1038/s41746-021-00388-6

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