Uncertainty Minimization and Feasibility Study for Managing the Complex and Interacting Anesthesia-Hemodynamic System

A novel control strategy is proposed to enable uncertainty minimization through knowledge infusion into the closed loop. The developed methodology aims the application of predictive control to regulate the complex anesthetic- hemodynamic (AH) interaction in a set of patients during general anesthesia. A special focus is given to solutions for minimizing the risk of instability arising from large uncertainty in the patient model dynamics. The paper explores the concept of digitalizing surgical actions as part of the natural mimicking strategy of actual anesthesiologists' real-life decision-making process. The simulations supporting the claims use an AH simulator. A feasibility study for solutions in an actual constrained input-output variable set is performed. Results confirm that the feasibility is enhanced when minimizing uncertainty conditions, having important clinical relevance in multi-drug optimization.