Extending ventilation duration estimations approach from adult to neonatal intensive care patients using artificial neural networks.
In earlier work, the research group successfully used artificial neural networks (ANNs) to estimate ventilation duration for adult intensive care unit (ICU) patients. The ANNs performed well in terms of correct classification rate (CCR) and average squared error (ASE) classifying the outcome into two classes: whether patients were ventilated for less than/equal to or for more than 8 h (< or >). The objective of new work was to apply this adult model to the estimation of ventilation with neonatal ICU (NICU) patient records. The performance obtained with the neonatal patients was comparable to that previously found with the adult database, again as measured in terms of a maximum CCR and a minimum ASE. The effectiveness of using the weight-elimination technique in controlling overfitting was again validated for the neonatal patients as it had been for our adult patients. It was concluded that the approach developed for ICU adult patients was also successfully applied to a different medical environment: neonatal ICU patients.
Tong
Yanling
Y
School of Information Technology and Engineering, University of Ottawa, ON, Canada.
Frize
Monique
M
Walker
Robin
R
D016208:Databases, Factual; D005240:Feasibility Studies; D006801:Humans; D007231:Infant, Newborn; D007363:Intensive Care Units, Neonatal; D008954:Models, Biological; D016571:Neural Networks, Computer; D012121:Respiration, Artificial / Q000379:methods / Q000706:statistics & numerical data*; D012680:Sensitivity and Specificity; D013997:Time Factors