Abstract
Bayesian networks have proved to be an appropriate tool for medical diagnosis, because uncertain reasoning in this field is based on a combination of causal knowledge and statistical data. However, a condition for the acceptance of a medical expert system is the ability to explain the diagnosis. This is a difficult task, because probabilistic inference seems to have little relation with human thinking. The current paper focuses on the graphic interface that constitutes one of the explanation capabilities of Elvira, a software tool for the edition and evaluation of graphical probabilistic models. The method we describe consists in working with different evidence cases and simultaneously displaying the corresponding probabilities.
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Lacave, C., Atienza, R., Diez, F.J. (2000). Graphical Explanation in Bayesian Networks. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_16
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DOI: https://doi.org/10.1007/3-540-39949-6_16
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