An intelligent model for the classification of children’s occupational therapy problems

摘要

ObjectivesIn Taiwan, the classification of real problems of children with appropriate occupational therapy is a difficult job for the therapist. The complexities of 127 attribute values to be evaluated in the assessment, the misleading diagnosis which may be made by the pediatrician and the shortage of manpower cause of high workload for the therapist. The design of an easy to use and effective classification model is therefore an important issue in children’s occupational therapy treatment. This study accordingly applies an artificial neural network (ANN) and classification and regression tree (CART) techniques to skeleton an intelligent classification model in order to provide a comprehensive framework to assist the therapist to raise the accuracy when categorizing children’s problems for occupational therapy. These categories with critical attributes under the guidelines of the American Occupational Therapy Association (AOTA) are discussed, in order to assist the therapist for precise assessment and appropriate treatment. To the best of our knowledge, no research has yet been conducted on the problems’ characteristics in children’s occupational therapy.