Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search
This addresses medical diagnosis for healthcare professionals and AI researchers, offering a potentially foundational framework, though it appears incremental in applying existing SP theory to a new domain.
The paper tackles medical diagnosis by proposing a novel approach based on the SP theory, which offers a simple disease representation format, handles errors and uncertainties, stores statistical data as frequencies, evaluates hypotheses with true probabilities, and facilitates unsupervised learning and AI integration.
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.