Name
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.
|
Year | DOI | Venue |
|---|---|---|
2000 | 10.1007/3-540-39949-6_16 | International Symposium on Medical Data Analysis |
Keywords | Field | DocType |
graphical probabilistic model,corresponding probability,current paper,software tool,bayesian network,bayesian networks,medical expert system,graphical explanation,probabilistic inference,medical diagnosis,appropriate tool,causal knowledge,probabilistic model,graphical interface | Probabilistic inference,Probabilistic logic network,Computer science,Expert system,Bayesian network,Graphical user interface,Artificial intelligence,Probabilistic logic,Graphical model,Medical diagnosis,Machine learning | Conference |
Volume | ISSN | ISBN |
1933 | 0302-9743 | 3-540-41089-9 |
Citations | PageRank | References |
7 | 0.62 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carmen Lacave | 1 | 154 | 12.81 |
| Roberto Atienza | 2 | 7 | 0.62 |
| Francisco Javier Díez | 3 | 150 | 18.73 |