Utilization of neural network in the elaboration of an evaluation scale for pain in cerebral palsy.

An interesting aspect of neural networks is shown in the elaboration of an evaluation scale for pain in cerebral palsy with severe mental retardation. Because of the diversity of cases, the number of items had to be limited in the final step of statistical validation. Classical analysis on prior data did not allow to decide whether the variability in results is more likely due to the type of disability (i.e., the possibility of pain expression) than to the actual presence of pain. A neural network was used to find implicit relations between the data, with the advantage of having total control on the variables' status by applying variations in the network architecture. This allowed for the rapid identification more significant item combinations as a function of degree of relationship to pain in cerebral palsy.