Probabilistic problem solving in biomedicine

With the current trend towards pervasive health care (see e.g. ), personalised health care (see e.g. ), and the ever growing amount of evidence coming from biomedical research, methods that can handle reasoning and learning under uncertainty are becoming more and more important. Probabilistic methods, and in particular Bayesian networks (BNs), have been introduced in the 1980s as a formalism for representing and reasoning with models of problems involving uncertainty, adopting probability theory as a basic framework. Since the beginning of the 1990s, researchers are exploring its possibilities for developing medical applications. An early example is the Pathfinder project, where a Bayesian network was developed for providing assistance with the identification of disorders from lymph-node tissue sections , which was later commercialised.

Source: Artificial Intelligence in Medicine - February 21, 2013 Category: Bioinformatics Authors: Arjen Hommersom, Peter J.F. Lucas Tags: Guest Editorial Source Type: research