Use of augmented decision tables to convert probabilistic data into clinical algorithms for the diagnosis of appendicitis

Abstract

Decision table techniques have been shown to be useful for ensuring logical completeness, eliminating ambiguity, and optimizing the translation of logic into flowcharts or computer programs. Nevertheless, they have not been widely applied in medicine. We have used decision table techniques to demonstrate the derivation of two sets of rules for determining whether to operate on patients with suspected appendicitis based on patterns of observed signs and symptoms. One rule set is based on a diagnostic threshold whereby morbidity is minimized; the other rule set minimizes mortality. For this purpose, we have developed an augmented decision table format that allows the incorporation of probability and utility data.