Intelligent Control of Closed-Loop Sedation in Simulated ICU Patients

By Brett L. Moore, E. Sinzinger, Todd M. Quasny, Larry D. Pyeatt. 2004

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Reinforcement Learning in Healthcare: A Survey (2019, methodology): “controlled variable, has enhanced individualized anesthetic management, resulting in the overall improvement of patient outcomes when compared with traditional controlled administration. Moore et al. [206], [207] applied TD Q() in administration of intravenous propofol in ICU settings, using the wel”
Reinforcement Learning for Closed-Loop Propofol Anesthesia: A Human Volunteer Study (2010, methodology): “Reinforcement learning (RL), another intelligent systems technique, has demonstrated proficiency in difficult robotic control tasks (Gullapalli 1993); however, RL has no reported application to clinical control problems, with the exception of work relating to this study (Moore et al. 2004).”
Individualization of pharmacological anemia management using reinforcement learning (2005, unknown): “(Schaeffer, Bailey, Shechter, & Roberts, 2004) reviewed various instances of medical application of Markov Decision Processes (MDP), the underlying control setting in RL. Most recently, ( Moore, Sinzinger, Quasny, & Pyeatt, 2004 ) demonstrated how RL can be successfully employed in closed-loop contr”
Incorporating prior knowledge into Q-learning for drug delivery individualization (2005, background): “However, applications of RL to drug administration have surfaced in the literature only very recently [6].”
Reinforcement learning for closed-loop propofol anesthesia: a study in human volunteers (2014, unknown): “Our simulation work supports this assertion (Moore, 2003).”
Reinforcement learning approach to individualization of chronic pharmacotherapy (2005, methodology): “A commonthread in themethods presented in[1], [4], [8], [9], [ 10 ] istheir use ofpharmacokinetic/pharmacodynamic (PK/PD) compartment models forpatient simulation.”