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01-12-2010 | Original Paper

A Novel Fuzzy Logic Inference System for Decision Support in Weaning from Mechanical Ventilation

Authors: Yusuf Alper Kilic, Ilke Kilic

Published in: Journal of Medical Systems | Issue 6/2010

Abstract

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.

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Title: A Novel Fuzzy Logic Inference System for Decision Support in Weaning from Mechanical Ventilation
Authors: Yusuf Alper Kilic
Ilke Kilic
Publication date: 01-12-2010
Publisher: Springer US
Published in: Journal of Medical Systems / Issue 6/2010
Print ISSN: 0148-5598
Electronic ISSN: 1573-689X
DOI: https://doi.org/10.1007/s10916-009-9327-0

At a glance: The STEP trials

Springer Medicine

A Novel Fuzzy Logic Inference System for Decision Support in Weaning from Mechanical Ventilation

Abstract

At a glance: The STEP trials

Springer Medicine

Abstract

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