Intelligent Control of Closed-Loop Sedation in Simulated ICU Patients

摘要

The intensive care unit is a challenging environment to both patient and caregiver. Continued shortages in staffing, principally in nursing, increase risk to patient and healthcare workers. To evaluate the use of intelligent systems in the improvement of patient care, an agent was developed to regulate ICU patient sedation. A temporal differencing form of reinforcement learning was used to train the agent in the administration of intravenous propofol in simulated ICU patients. The agent utilized the well-studied Marsh-Schnider pharmacokinetic model to estimate the distribution of drug within the patient. A pharmacodynamic model then estimated drug effect. A processed form of electroencephalogram, the bispectral index, served as the system control variable. The agent demonstrated satisfactory control of the simulated patient's consciousness level in static and dynamic setpoint conditions. The agent demonstrated superior stability and responsiveness when compared to a well-tuned PID controller, the control method of choice in closed-loop sedation control literature.