Abstract
A hybrid knowledge-and-model-based advisory system for intensive care ventilators is being developed. The system consists of two parts: a knowledge-based toplevel module using neural fuzzy technology and a model-based lower-level module consisting of 4 sub-units. The system generates advice on four ventilator settings (the inspired fraction of oxygen (FiO2) positive end-expiratory pressure (PEEP). peak inspiratory pressure (PINSP) and ventilatory rate) based on the patient's routine and cardio-respiratory measurements. The validation results of the top-level module were encouraging. Validation of the integrated system using retrospective clinical data is underway.
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