A HYBRID KNOWLEDGE-AND-MODEL-BASED VENTILATORY ADVISORY SYSTEM

摘要

A hybrid knowledge-and-model-based advisory system for intensive care ventilators is being developed. The system consists of two parts: a knowledge-based top-level 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 (FiO_2), 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.