Reinforcement Learning: A Novel Method for Optimal Control of Propofol-Induced Hypnosis

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “o maintain physiological stability and lowering pains of patients Moore et al. [206], [207] Q() N/A N/A PK/PD model Achieving superior stability compared to a well-tuned PID controller. Moore et al. [208], [209] Q-learning N/A N/A PK/PD model Using the change of BIS as the state representation. Moor”
Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers (2014, methodology): “However, RL has no reported application to clinical control problems, with the exception of work leading to this study (Moore et al., 2011a,b, 2004).”
Reinforcement Learning Versus Proportional–Integral–Derivative Control of Hypnosis in a Simulated Intraoperative Patient (2011, methodology): “We applied Watkins Q-Learning to estimate the value function for each of the propofol infusion rates.(21) Equation 2 presents the Q-learning update rule: Q st, at 4Q st, at rt 5 maxa Q st 5, a”