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 expiratory times, peak inspiratory and positive end-expiratory pressures and inspired oxygen concentration), partial pressures of arterial blood gases and pH. Two hidden layers comprising 50 nodes each are employed in the network, which utilizes a standard back-propagation algorithm. The network provides the new ventilator settings as five outputs that represent the most appropriate ventilator settings projected to maintain blood gases within an acceptable range. The network has been trained using a data set derived from a rule-based expert system developed for the same purpose. Performances of both systems have been compared. The neural network is capable of learning and adapting to the individual patient's response, which in principle offers significant advantages over the rule-based system.
Snowden
S
S
Department of Medical Physics, St James's University Hospital, Leeds, UK.
Brownlee
K G
KG
Smye
S W
SW
Dear
P R
PR
D001784:Blood Gas Analysis; D006801:Humans; D006863:Hydrogen-Ion Concentration; D007231:Infant, Newborn; D007385:Intermittent Positive-Pressure Ventilation / Q000295:instrumentation*; D008991:Monitoring, Physiologic; D016571:Neural Networks, Computer; D012985:Software Design