HIERARCHICAL MODELING IN MEDICAL ABNORMALITY DETECTION

摘要

Hierarchal modeling is used to distinguish one state (26, 28, 32, 34, 38, 40, 44, 46) or class from three or more classes. In a first stage, a normal (26) or other class is distinguished from a diseased (28) or other groups of classes. If the results of the first stage classification indicate diseased (28) or data within the groups of different classes, a subsequent stage of classification is performed. In a subsequent stage of classification, the data is classified to distinguish one or more other classes (32, 34, 38, 40, 44, 46) from the remaining classes. Using two or more stages, medical information is classified by eliminating one or more possible classes in each stage to finally identify a particular class (26, 28, 32, 34, 38, 40, 44, 46) most appropriate or probable for the data.