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In medical problem solving two main subproblems can be identified, namely the selection of possible hypotheses from some initial data, and the evaluation of these hypothesis, possibly taking into account some additional evidence. In a number of model-based systems, more superficial knowledge is used for generating diagnostic hypotheses, whereas deeper knowledge is used to assess them, but these systems are usually domain specific. In this paper we present a generic methodology for representing causal knowledge, which enables diagnostic reasoning to be made at the appropriate knowledge depth. In particular, to obtain candidate diagnoses from easily observable findings (superficial knowledge) and to assess them subsequently via less easily observable findings (e.g. signs and results from complementary tests, usually associated with deeper domain entities).
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