Extension of the Hepar II Model to Multiple-Disorder Diagnosis
The Hepar II system is based on a Bayesian network model of a subset of the domain of hepatology in which the structure of the network is elicited from an expert diagnostician and the parameters are learned from a database of medical cases. The model follows the assumption made in the database that each patient case is diagnosed with a single disorder, i.e., disorders are mutually exclusive.
In this paper, we describe an extension of the Hepar II system to multiple-disorder diagnosis. We show that our network transforms readily to a network that can perform multiple-disorder diagnosis with some benefits to the quality of numerical parameters learned from the database. We demonstrate empirically that the diagnostic performance in terms of single-disorder diagnosis improves under this transformation. The new model is more realistic and we expect that it will be of higher value in clinical practice.
KeywordsBayesian Network Numerical Parameter Liver Disorder Conditional Probability Distribution Bayesian Network Model
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