AN ADVISORY SYSTEM FOR ARTIFICIAL-VENTILATION OF THE NEWBORN UTILIZING A NEURAL-NETWORK

A neural network has been developed to manage ventilated neonates. The network inputs are the current ventilator settings (inspiratory and e xpiratory times, peak inspiratory and positive end-expiratory pressure s and inspired oxygen concentration), partial pressures of arterial bl ood gases and pH. Two hidden layers comprising 50 nodes each are emplo yed in the network, which utilizes a standard back-propagation algorit hm. The network provides the new ventilator settings as five outputs t hat represent the most appropriate ventilator settings projected to ma intain blood gases within an acceptable range. The network has been tr ained using a data set derived from a rule-based expert system develop ed for the same purpose. Performances of both systems have been compar ed. The neural network is capable of learning and adapting to the indi vidual patient's response, which in principle offers significant advan tages over the rule-based system.