Reinforcement Learning: A Novel Method for Optimal Control of Propofol-Induced Hypnosis
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...
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| Veröffentlicht in: | Anesthesia and analgesia 2011-02, Vol.112 (2), p.360-367 |
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| container_title | Anesthesia and analgesia |
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| creator | Moore, Brett L. Doufas, Anthony G. Pyeatt, Larry D. |
| description | 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. |
| doi_str_mv | 10.1213/ANE.0b013e31820334a7 |
| format | Article |
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Cell therapy and gene therapy</subject><subject>Anesthetics, Intravenous - administration & dosage</subject><subject>Anesthetics, Intravenous - pharmacokinetics</subject><subject>Artificial Intelligence</subject><subject>Biological and medical sciences</subject><subject>Consciousness Monitors</subject><subject>Dose-Response Relationship, Drug</subject><subject>Humans</subject><subject>Hypnosis, Anesthetic</subject><subject>Intraoperative Period</subject><subject>Medical sciences</subject><subject>Models, Theoretical</subject><subject>Monitoring, Intraoperative - instrumentation</subject><subject>Monitoring, Intraoperative - methods</subject><subject>Pattern Recognition, Automated</subject><subject>Propofol - administration & dosage</subject><subject>Propofol - pharmacokinetics</subject><subject>Signal Processing, Computer-Assisted</subject><issn>0003-2999</issn><issn>1526-7598</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNpdkU1v1DAQhi1ERbeFf4CQL4hTWo-dxDG31aq0lZa2QiCOltcZswGvHeyEqv8er7oUqXMZjfTMx_sOIW-BnQEHcb68uThjGwYCBXScCVEb-YIsoOFtJRvVvSQLxpiouFLqmJzk_LOUwLr2FTnmAE2runpBvn_BIbiYLO4wTHSNJoUh_PhIl_Qm_kFPP-O0jT0tCL0dp2FnPF3FMKXoaXT0LsUxuuir69DPFnt69TCGmIf8mhw54zO-OeRT8u3TxdfVVbW-vbxeLdeVFaIc13MupasbyTkq7ASXNXBnocjpAd3GQtvaHgCl7VpwqjegOsmbotcIY5U4JR8e544p_p4xT3o3ZIvem4BxzrqrJZdKKFnI-pG0Keac0OkxFTnpQQPTe0d1cVQ_d7S0vTssmDc77J-a_llYgPcHwGRrvEsm2CH_54RUAEXQ0_776CdM-Zef7zHpLRo_bTXbRyNUxVmBeSmq_buE-Av7VI1v</recordid><startdate>20110201</startdate><enddate>20110201</enddate><creator>Moore, Brett L.</creator><creator>Doufas, Anthony G.</creator><creator>Pyeatt, Larry D.</creator><general>International Anesthesia Research Society</general><general>Lippincott Williams & Wilkins</general><scope>IQODW</scope><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20110201</creationdate><title>Reinforcement Learning: A Novel Method for Optimal Control of Propofol-Induced Hypnosis</title><author>Moore, Brett L. ; Doufas, Anthony G. ; Pyeatt, Larry D.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3303-d2277f45722e9e8327412fc1033d1efbc166cd11e7c861f9da198725203a3ac93</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><topic>Anesthesia</topic><topic>Anesthesia, Closed-Circuit</topic><topic>Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy</topic><topic>Anesthetics, Intravenous - administration & dosage</topic><topic>Anesthetics, Intravenous - pharmacokinetics</topic><topic>Artificial Intelligence</topic><topic>Biological and medical sciences</topic><topic>Consciousness Monitors</topic><topic>Dose-Response Relationship, Drug</topic><topic>Humans</topic><topic>Hypnosis, Anesthetic</topic><topic>Intraoperative Period</topic><topic>Medical sciences</topic><topic>Models, Theoretical</topic><topic>Monitoring, Intraoperative - instrumentation</topic><topic>Monitoring, Intraoperative - methods</topic><topic>Pattern Recognition, Automated</topic><topic>Propofol - administration & dosage</topic><topic>Propofol - pharmacokinetics</topic><topic>Signal Processing, Computer-Assisted</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Moore, Brett L.</creatorcontrib><creatorcontrib>Doufas, Anthony G.</creatorcontrib><creatorcontrib>Pyeatt, Larry D.</creatorcontrib><collection>Pascal-Francis</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Anesthesia and analgesia</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Moore, Brett L.</au><au>Doufas, Anthony G.</au><au>Pyeatt, Larry D.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Reinforcement Learning: A Novel Method for Optimal Control of Propofol-Induced Hypnosis</atitle><jtitle>Anesthesia and analgesia</jtitle><addtitle>Anesth Analg</addtitle><date>2011-02-01</date><risdate>2011</risdate><volume>112</volume><issue>2</issue><spage>360</spage><epage>367</epage><pages>360-367</pages><issn>0003-2999</issn><eissn>1526-7598</eissn><coden>AACRAT</coden><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.</abstract><cop>Hagerstown, MD</cop><pub>International Anesthesia Research Society</pub><pmid>21156984</pmid><doi>10.1213/ANE.0b013e31820334a7</doi><tpages>8</tpages></addata></record> |
| fulltext | fulltext |
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| ispartof | Anesthesia and analgesia, 2011-02, Vol.112 (2), p.360-367 |
| issn | 0003-2999 1526-7598 |
| language | eng |
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| source | MEDLINE; Journals@Ovid LWW Legacy Archive; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals |
| subjects | Anesthesia Anesthesia, Closed-Circuit Anesthesia. Intensive care medicine. Transfusions. Cell therapy and gene therapy Anesthetics, Intravenous - administration & dosage Anesthetics, Intravenous - pharmacokinetics Artificial Intelligence Biological and medical sciences Consciousness Monitors Dose-Response Relationship, Drug Humans Hypnosis, Anesthetic Intraoperative Period Medical sciences Models, Theoretical Monitoring, Intraoperative - instrumentation Monitoring, Intraoperative - methods Pattern Recognition, Automated Propofol - administration & dosage Propofol - pharmacokinetics Signal Processing, Computer-Assisted |
| title | Reinforcement Learning: A Novel Method for Optimal Control of Propofol-Induced Hypnosis |
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