A multiobjective design of a patient and anaesthetist-friendly neuromuscular blockade controller.
During surgeries (especially in long ones), patients are subject to a substantial amount of drug dosage necessary to achieve the required neuromuscular blockade level. This paper aims at the development of a fuzzy controller that satisfies two important goals: 1) an optimization of the amount of drug (atracurium) required to induce an adequate level of relaxation and 2) a concomitant ability to explain the undertaken control decision at the level of natural language. For instance, statements of the form "Since the difference between the target and the current blockade level is near zero, a small quantity of drug infusion is currently being applied", where "near zero" and "small" are linguistic terms that are represented as fuzzy sets. In this sense, we can regard this controller as a construct that is human friendly and highly interpretable (transparent). To address the two objectives outlined above, we consider the use of a multiobjective evolutionary optimization. How the quality of the control action and the controller interpretability are formalized and captured in this optimization framework is presented. The effectiveness of the approach is demonstrated through a comprehensive suite of experiments involving 100 simulated patients (used for training) and 500 patients (forming the test set), validating the approach for application in the operating theater.
Fazendeiro
Paulo
P
Department of Informatics, University of Beira Interior, Covilha 6200-001, Portugal. fazendeiro@ubi.pt
Valente de Oliveira
José
J
Pedrycz
Witold
W
D000758:Anesthesia / Q000379:methods; D003198:Computer Simulation; D020000:Decision Support Systems, Clinical; D004360:Drug Therapy, Computer-Assisted / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D019148:Neuromuscular Blockade / Q000379:methods*; D009466:Neuromuscular Blocking Agents / Q000008:administration & dosage*; D012984:Software; D012985:Software Design
D009466:Neuromuscular Blocking Agents