Reinforcement learning derived chemotherapeutic schedules for robust patient-specific therapy

Abstract The in-silico development of a chemotherapeutic dosing schedule for treating cancer relies upon a parameterization of a particular tumour growth model to describe the dynamics of the cancer in response to the dose of the drug. In practice, it is often prohibitively difficult to ensure the v...

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Autores principales: Brydon Eastman, Michelle Przedborski, Mohammad Kohandel
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
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Acceso en línea:https://doaj.org/article/346e4b6869474fd3982e56778d98ce5a
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