Rule-base derivation for intensive care ventilator control using ANFIS

Abstract

In recent years, much research has been done on the use of fuzzy systems in medicine. The fuzzy rule-bases have usually been derived after extensive discussion with the clinical experts. This takes a lot of time from the clinical experts and the knowledge engineers. This paper presents the use of the adaptive neuro-fuzzy inference system (ANFIS) in rule-base derivation for ventilator control. The change of the inspired fraction of oxygen (FiO 2) advised by eight clinical experts responding to 71 clinical scenarios was recorded. ANFIS and a multilayer perceptron (MLP) were then used to model the relationship between the inputs (the arterial oxygen tension (PaO 2), FiO 2 and the positive end-expiratory pressure (PEEP) level) and the change in FiO 2 suggested. Compared to a previous fuzzy advisor (FAVeM), both the ANFIS and the MLP were found to correlate with the clinicians’ decision better (correlation coefficient of 0.694 and 0.701, respectively compared to 0.630). A formerly developed model-based radial basis network advisor (RBN-MB) was used for comparison. Closed-loop simulations showed that the ANFIS, MLP and the RBN-MB’s performance were comparable to the clinicians’ performance (correlation coefficients of 0.852, 0.962 and 0.787, respectively). The FAVeM’s performance differed from the clinicians’ performance (correlation coefficient of 0.332) but the resulting PaO 2 was still within safety limits.