Learning Bayesian-Network Topologies in Realistic Medical Domains

Authors:
Xiaofeng Wu;Peter J. F. Lucas;Susan Kerr;Roelf Dijkhuizen
Affiliations:
-;-;-;-
Venue:
ISMDA '01 Proceedings of the Second International Symposium on Medical Data Analysis
Year:
2001

Citing 3
Cited 0

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Abstract

In recent years, a number of algorithms have been developed for learning the structure of Bayesian networks from data. In this paper we apply some of these algorithms to a realistic medical domain--stroke. Basically, the domain of stroke is taken as a typical example of a medical domain where much data are available concerning a few hundred patients. Learning the structure of a Bayesian network is known to be hard under these conditions. In this paper, two different structure learning algorithms are compared to each other. A causal model which was constructed with the help of an expert clinician is adopted as the gold standard. The advantages and limitations of various structure-learning algorithms are discussed in the context of the experimental results obtained.