Abstract
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.
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References
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© 2000 Physica-Verlag Heidelberg
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Oniśko, A., Druzdzel, M.J., Wasyluk, H. (2000). Extension of the Hepar II Model to Multiple-Disorder Diagnosis. In: Intelligent Information Systems. Advances in Soft Computing, vol 4. Physica, Heidelberg. https://doi.org/10.1007/978-3-7908-1846-8_27
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DOI: https://doi.org/10.1007/978-3-7908-1846-8_27
Publisher Name: Physica, Heidelberg
Print ISBN: 978-3-7908-1309-8
Online ISBN: 978-3-7908-1846-8
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