AAAI Publications, Twenty-Second IAAI Conference

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Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Human Volunteer Study
Brett L. Moore, Periklis Panousis, Vivek Kulkarni, Larry D. Pyeatt, Anthony G. Doufas

Last modified: 2010-07-05

Abstract


Research has demonstrated the efficacy of closed-loop control of anesthesia using the bispectral index (BIS) of the electroencephalogram as the controlled variable, and the development of model-based, patient-adaptive systems has considerably improved anesthetic control. To further explore the use of model-based control in anesthesia, we investigated the application of reinforcement learning (RL) in the delivery of patient-specific, propofol-induced hypnosis in human volunteers. When compared to published performance metrics, RL control demonstrated accuracy and stability, indicating that further, more rigorous clinical study is warranted.

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