title: Reinforcement learning: a novel method for optimal control of propofol-induced hypnosis.
abstract: Reinforcement learning (RL) is an intelligent systems technique with a history of success in difficult robotic control problems. Similar machine learning techniques, such as artificial neural networks and fuzzy logic, have been successfully applied to clinical control problems. Although RL presents a mathematically robust method of achieving optimal control in systems challenged with noise, nonlinearity, time delay, and uncertainty, no application of RL in clinical anesthesia has been reported.
authors.affiliation: Department of Computer Science, Texas Tech University, P.O. Box 43104, Lubbock, TX 79409-3104, USA.
authors.forename: Brett L
authors.initials: BL
authors.lastname: Moore
authors.forename: Anthony G
authors.initials: AG
authors.lastname: Doufas
authors.forename: Larry D
authors.initials: LD
authors.lastname: Pyeatt
mesh_terms: D000764:Anesthesia, Closed-Circuit; D018686:Anesthetics, Intravenous / Q000008:administration & dosage* / Q000493:pharmacokinetics; D001185:Artificial Intelligence; D056805:Consciousness Monitors; D004305:Dose-Response Relationship, Drug; D006801:Humans; D006991:Hypnosis, Anesthetic; D007432:Intraoperative Period; D008962:Models, Theoretical; D016343:Monitoring, Intraoperative / Q000295:instrumentation / Q000379:methods; D010363:Pattern Recognition, Automated; D015742:Propofol / Q000008:administration & dosage* / Q000493:pharmacokinetics; D012815:Signal Processing, Computer-Assisted
chemical_list: D018686:Anesthetics, Intravenous; D015742:Propofol