Multilayer feedforward networks are universal approximators
Neural Networks
A simple but powerful heuristic method for generating fuzzy rules from numerical data
Fuzzy Sets and Systems
Design of self-learning fuzzy sliding mode controllers based on genetic algorithms
Fuzzy Sets and Systems
A fuzzy-logic-based approach to qualitative modeling
IEEE Transactions on Fuzzy Systems
On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm
IEEE Transactions on Neural Networks
Methodological review: Intelligent decision support systems for mechanical ventilation
Artificial Intelligence in Medicine
Knowledge and intelligent computing system in medicine
Computers in Biology and Medicine
Applied Soft Computing
A survey on use of soft computing methods in medicine
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Adaptive Neuro-Fuzzy Inference System for diagnosis risk in dengue patients
Expert Systems with Applications: An International Journal
A novel brain-inspired neuro-fuzzy hybrid system for artificial ventilation modeling
Expert Systems with Applications: An International Journal
Implementing an automated ventilation guideline using the semantic wiki KnowWE
EKAW'12 Proceedings of the 18th international conference on Knowledge Engineering and Knowledge Management
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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.