Multiple disease (fault) diagnosis with applications to the QMR-DT problem
Feili Yu, Fang Tu, Haiying Tu, Krishna Rao Pattipati · 2004
In this paper, we present three classes of computationally efficient algorithms that can handle cases with hundreds of positive findings in QMR-DT(Quick Medical Reference, Decision-Theoretic) Network. These include Lagrangian Relaxation Algorithm (LRA), Primal Heuristic Algorithm (PHA), and Approximate Belief Revision Algorithm (ABR). These algorithms solve the QMR-DT problem by finding the most likely set of diseases given the findings. Extensive computational experiments have shown that LRA obtains the best solutions among the three algorithms proposed within a relatively small processing time. We also show that the Variational Probabilistic Inference method is a special case of our LRA. The solutions are generic and have application to multiple fault diagnosis in complex industrial systems.