Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Human Volunteer Study - Brett L. Moore, Brett L. Moore; Panousis, Periklis; Kulkarni, Vivek; Pyeatt, Larry; Anthony G. Doufas, Anthony G. Doufas (Proceedings of the AAAI Conference on Artificial Intelligence, 2010)
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.