Medical diagnosis as pattern recognition in a framework of information
  compression by multiple alignment, unification and search
  This paper describes a novel approach to medical diagnosis based on the SP
theory of computing and cognition. The main attractions of this approach are: a
format for representing diseases that is simple and intuitive; an ability to
cope with errors and uncertainties in diagnostic information; the simplicity of
storing statistical information as frequencies of occurrence of diseases; a
method for evaluating alternative diagnostic hypotheses that yields true
probabilities; and a framework that should facilitate unsupervised learning of
medical knowledge and the integration of medical diagnosis with other AI
applications.
