Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently, researchers have favored computer-automated drug delivery system to reduce the risks of intraoperative awareness and postoperative morbidity, and their use is associated with a number of favorable patient outcomes. For example, Proportional-Integral-Derivative, maximum a posteriori (MAP) Bayesian approaches, fuzzy logic, and reinforcement learning, have been developed and applied successfully in simulated patients or volunteers. Despite the successes, variations of errors in the observed data are known to affect the performances of the controllers, especially when a patient state estimation is required. To have a better controller,  apply Partially Observable Markov Decision Process (POMDP) to achieve better drug delivery policy, even when there is incomplete information about patients' current states during operations. , a POMDP model for closed-loop anesthesia control is introduced. Then, a state-of-the-art POMDP solver is used to compute a good control policy, in other words, propofol rates to administer to a patient, in efforts to reduce the risk of intraoperative awareness and postoperative side effects in patients.
['Eddy C. Borera', 'Brett L. Moore', 'Larry D. Pyeatt']
I am writing a research paper on a idea Partially Observable Markov Decision Process for closed-loop anesthesia control
[{"from": "human", "value": "I am writing a research paper on a idea Partially Observable Markov Decision Process for closed-loop anesthesia control"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Recently, researchers have favored computer-automated drug delivery system to reduce the risks of intraoperative awareness and postoperative morbidity, and their use is associated with a number of favorable patient outcomes. For example, Proportional-Integral-Derivative, maximum a posteriori (MAP) Bayesian approaches, fuzzy logic, and reinforcement learning, have been developed and applied successfully in simulated patients or volunteers. Despite the successes, variations of errors in the observed data are known to affect the performances of the controllers, especially when a patient state estimation is required. To have a better controller,  apply Partially Observable Markov Decision Process (POMDP) to achieve better drug delivery policy, even when there is incomplete information about patients' current states during operations. , a POMDP model for closed-loop anesthesia control is introduced. Then, a state-of-the-art POMDP solver is used to compute a good control policy, in other words, propofol rates to administer to a patient, in efforts to reduce the risk of intraoperative awareness and postoperative side effects in patients."}]