The introduction of Bayesian Networks (BNs) - also known as Belief Nets or Belief Networks - for medical diagnosis dated back in the 80s. BNs have been used as a formalism for representing and reasoning in problems involving uncertainty via the use of graphical models; within such structures, probability theory is adopted as a basic framework [1]. The Bayesian Network (BN) formalism offers a natural way to represent the uncertainty involved in medicine when attempting to deliver a diagnosis [2]. This is due to the fact that the dependencies between signs or symptoms and possible diagnoses, as well as the probabilistic interaction among the data, can be easily described in a Bayesian Network (BN). As the formalism offers this natural representation, any probabilistic statement that may concern both individual and combinations of variables can be computed from a properly structured BN. An example of the BN formalism for medical diagnosis is presented
in this thesis.
[1] J. Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible
Inference. Morgan Kaufmann Publishers Inc, 1988.
[2] P. J. F. Lucas, H. Boot, and B. Taal. A Decision Theoretic Network Approach
to Treatment Management and Prognosis. Knowledge-Based Systems, 11(5–
6):321–330, November 1998.