A novel fuzzy logic inference system for decision support in weaning from mechanical ventilation.
Weaning from mechanical ventilation represents one of the most challenging issues in management of critically ill patients. Currently used weaning predictors ignore many important dimensions of weaning outcome and have not been uniformly successful. A fuzzy logic inference system that uses nine variables, and five rule blocks within two layers, has been designed and implemented over mathematical simulations and random clinical scenarios, to compare its behavior and performance in predicting expert opinion with those for rapid shallow breathing index (RSBI), pressure time index and Jabour' weaning index. RSBI has failed to predict expert opinion in 52% of scenarios. Fuzzy logic inference system has shown the best discriminative power (ROC: 0.9288), and RSBI the worst (ROC: 0.6556) in predicting expert opinion. Fuzzy logic provides an approach which can handle multi-attribute decision making, and is a very powerful tool to overcome the weaknesses of currently used weaning predictors.
Kilic
Yusuf Alper
YA
Department of General Surgery, Hacettepe University Faculty of Medicine, Ankara, Turkey. yusufa@hacettepe.edu.tr
Kilic
Ilke
I
D020000:Decision Support Systems, Clinical; D017143:Fuzzy Logic; D006801:Humans; D015300:Ventilator Weaning