Computational prognostic models are increasingly used in medicine to predict the natural course of disease, or the expected outcome after treatment.
Prognosis forms an integral part of systems for treatment selection and treatment planning.
Furthermore, prognostic models may play an important role in guiding diagnostic problem solving, e.g. by only requesting information concerning tests, of which the outcome affects knowledge of the prognosis. Examples of such domain models are causal disease models, and physiological models of regulatory mechanisms in the human body. Various methods have been suggested for the representations of such domain models ranging from quantitative and probabilistic approaches to symbolic and qualitative ones. Semantic concepts such as time, e.g. for modelling the progressive changes of regulatory mechanisms, have formed an important and challenging modelling issue. Moreover, automatic learning techniques of such models have been proposed.