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- W2229272015 startingPage "1807" @default.
- W2229272015 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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- W2229272015 date "2010-07-11" @default.
- W2229272015 modified "2023-09-26" @default.
- W2229272015 title "Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Human Volunteer Study" @default.
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- W2229272015 doi "https://doi.org/10.1609/aaai.v24i2.18817" @default.
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