Various formal theories have been proposed in the literature to capture the notions of diagnosis underlying diagnostic programs. Examples of such notions are: heuristic classification, which is used in systems incorporating empirical knowledge, and model-based diagnosis, which is used in diagnostic systems based on detailed domain models. Typically, such domain models include knowledge of causal, structural, and functional interactions among modelled objects. In this paper, a new set-theoretical framework for the analysis of diagnosis is presented. Basically, the framework distinguishes between `evidence functions', which characterize the net impact of knowledge bases for purposes of diagnosis, and `notions of diagnosis', which define how evidence functions are to be used to map findings observed for a problem case to diagnostic solutions. This set-theoretical framework offers a simple, yet powerful tool for comparing existing notions of diagnosis, as well as for proposing new notions ...

Prognosis -- the prediction of the course and outcome of disease processes -- plays an important role in patient management tasks like diagnosis and treatment planning. Prognostic models form, therefore, an integral part of a number of systems supporting these tasks. Furthermore, prognostic models constitute instruments to evaluate the quality of health care and the consequences of health care policies by comparing predictions according to care norms with actual results. Approaches to developing prognostic models vary from using traditional probabilistic techniques, originating from the field of Statistics, to more qualitative and model-based techniques, originating from the eld of Artificial Intelligence. In this paper, various approaches to constructing prognostic models, with emphasis on methods from the field of Artificial Intelligence, are described and compared.

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This is a sample paper for the workshop on Prognostic Models in Medicine: Arti- cial Intelligence and Decision-analytic Approaches held during AIMDM'99. Prognosis { the prediction of the course and outcome of disease processes { is an important aspect in patient management tasks like diagnosis and treatment planning. Approaches to prognosis vary from traditional probabilistic decision-theoretic approaches, originating from the eld of Medical Decision Analysis, to more qualitative and modelbased approaches originating from the eld of Articial Intelligence. 1 Introduction This is a sample paper, based on bits and pieces of a paper that was published in the proceedings of CESA '98 [ 1 ] . Computational prognostic models are increasingly used in Medicine to predict the natural course and the outcome of illness, possibly following treatment. Examples of outcome prediction include life expectancy, recurrence of disease and unwanted side eects of drugs. In this paper, th...

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