Abstract:One of the most accountable methods of providing machine assistance in medical diagnosis is to retrieve and display similar previously diagnosed cases from a database. In practice, however, classifying cases according to the diagnoses of their nearest neighbours is often significantly less accurate than other statistical classifiers. In this paper the transparency of the nearest neighbours method is combined with the accuracy of another statistical method. This is achieved by using the other statistical method to define a measure of similarity between the presentations of two cases. The diagnosis of abdominal pain of suspected gynaecological origin is used as a case study to evaluate this method. Bayes’ theorem, with the usual assumption of conditional independence, is used to define a metric on cases. This new metric was found to correspond as well as Hamming distance to the clinical notion of “similarity” between cases, while significantly increasing accuracy to that of the Bayes’ method itself.
We present a case study of the use of formal specification techniques in the design of a knowledge-based system. The Z specification notation [ll] is used in the development of a medical flow chart program, beginning with a highly abstract notion ofinference and proceeding by refinement toward a particular implementation for flow charts. Diagnostic inference is modelled b y idempotent, decreasing functions. The flow chart was found to be significantly more accurate than a statistical classifier (Independence Bayes).Formal specification provides several benefits. Design decisions are made explicit and are documented concisely, and the results of making alternative choices can be explored within the same framework. The formal specification also suggests approaches that probably would not otherwise have been consadered.
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