RESUMO
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.
Assuntos
Anestesia com Circuito Fechado , Anestésicos Intravenosos/administração & dosagem , Inteligência Artificial , Monitores de Consciência , Hipnose Anestésica , Modelos Teóricos , Monitorização Intraoperatória , Propofol/administração & dosagem , Anestésicos Intravenosos/farmacocinética , Relação Dose-Resposta a Droga , Humanos , Período Intraoperatório , Monitorização Intraoperatória/instrumentação , Monitorização Intraoperatória/métodos , Reconhecimento Automatizado de Padrão , Propofol/farmacocinética , Processamento de Sinais Assistido por ComputadorRESUMO
Research has demonstrated the efficacy of closed-loop control of anesthesia using bispectral index (BIS) as the controlled variable, and the recent 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 fuzzy control in the delivery of patient-specific propofol-induced hypnosis. In simulated intraoperative patients, the fuzzy controller demonstrated clinically acceptable performance, suggesting that further study is warranted.