Reinforcement Learning with Action-Derived Rewards for Chemotherapy and Clinical Trial Dosing Regimen Selection

By Gregory Yauney, Pratik Shah. 2018

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “cal or generic cancer clinical trial Goldberg & Kosorok [96], Soliman [97] Q-learning N/A N/A Linear model Addressing problems with censored data and a ﬂexible number of stages. Yauney & Shah [98] Q-learning N/A DRL (Double DQN) ODE model Addressing the problem of unstructured outcome reward”
Artificial Intelligence for Clinical Trial Design. (2019, methodology): “In a previous study, Shah et al. evaluated the significance and efficacy of clinical evidence generated from advanced technology-enabled non-invasive diagnostic screening (TES) using low-cost 584 Trends in Pharmacological Sciences, August 2019, Vol. 40, No. 8 smartphones and other point-of-care medi”
VariBAD: A Very Good Method for Bayes-Adaptive Deep RL via Meta-Learning (2019, background): “Balancing this trade-off is key to maximising expected return during learning, which is desirable in many settings, particularly in high-stakes real-world applications like healthcare and education (Liu et al., 2014; Yauney & Shah, 2018).”
Artificial intelligence in cancer target identification and drug discovery (2022, methodology): “For example, Shah et al 261 construct an arti ﬁ cial intelligence system that made use of the ‘ self-learning ’ deep reinforcement learning technology to looks at treatment regimens currently in use, and iteratively adjusts the doses.”
ARTIFICIAL INTELLIGENCE AND PERSONALIZED MEDICINE (2019, background): “AI can be exploited in relevant large scale population trials that seek to minimize the deployment and use of inappropriate drugs or interventions studies for each participant in the trial, as described recently by Yauney and Shah.[39] Of great interest in this context are the design and conduct of ”