Diagnosing Multiple Interacting Defects with Combination Descriptions

Abstract

Conventional diagnostic methods assume that only a single defect is present. Cases with multiple defects can be difficult to diagnose because the defects can interact, meaning that the observable cues are not a sum of the cues for the component defects. Diagnostic methods that use cue-to-defect relationships fail when interactions between defects change the observable cues. Model-based methods can be used to diagnose multiple defects by simulating the results of interactions. Model-based methods, however, are limited to domains with accurate and complete models as well as enough available data to initialize the models. This research develops a description and classification of the ways cues change when defects interact, based on physical interactions and example cases. Each type of cue may combine in a different way, so each has a separate description. Also developed is a formula for quantifying the amount of abnormal data explained by a set of defects, termed explanation points. The descriptions and formula are used in a domain-independent computational diagnostic model that can diagnose multiple defects, even when cues are altered or missing due to interactions between the defects. In the medical domain investigated in this research (diagnosis of congenital heart defects), we found that cues combine with one another in a small number of ways: all cues may appear, the values of the cues may be added, or dominant cues may mask any other cues present. Cues of each type combine in one of these basic ways, or use a combination of a few of the basic ways, based on characteristics of the cues, case, or domain. The diagnostic model is tested by constructing a program with a knowledge base in Pediatric Cardiology and testing it on cases of single and multiple defects from hospital files. This program correctly diagnoses cases with multiple interacting defects for which current methods are not applicable or fail.