Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search
Artificial Intelligence
2014-09-30 v1
Abstract
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.
Keywords
Cite
@article{arxiv.1409.8053,
title = {Medical diagnosis as pattern recognition in a framework of information compression by multiple alignment, unification and search},
author = {J. Gerard Wolff},
journal= {arXiv preprint arXiv:1409.8053},
year = {2014}
}