Using Temporal Context-Specific Independence Information in the Exploratory Analysis of Disease Processes

Abstract. Disease processes in patients are temporal in nature and involve uncertainty. It is necessary to gain insight into these processes when aiming at improving the diagnosis, treatment and prognosis of disease in patients. One way to achieve these aims is by explicitly modelling disease processes; several researchers have advocated the use of dynamic Bayesian networks for this purpose because of the versatility and expressiveness of this time-oriented probabilistic formalism. In the research described in this paper, we investigate the role of contextspecific independence information in modelling the evolution of disease. The hypothesis tested was that within similar populations of patients differences in the learnt structure of a dynamic Bayesian network may result, depending on whether or not patients have a particular disease. This is an example of temporal context-specific independence information. We have tested and confirmed this hypothesis using a constraintbased Bayesian n...

• Type: Conference
• Year: 2007
• Where: AIME
• Authors: Stefan Visscher, Peter J. F. Lucas, Ildikó Flesch, Karin Schurink
• Keywords: AIME 2007, Artificial Intelligence, Bayesian Network, Disease Processes, Dynamic Bayesian Network