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dc.contributor.authorMARSH, DWRen_US
dc.contributor.authorKyrimi, Een_US
dc.contributor.authorConference on Probabilistic Graphical Modelsen_US
dc.date.accessioned2016-09-08T10:46:49Z
dc.date.available2016-07-04en_US
dc.date.submitted2016-08-08T13:02:43.974Z
dc.identifier.issn2640-3498en_US
dc.identifier.urihttp://qmro.qmul.ac.uk/xmlui/handle/123456789/15046
dc.description.abstractMany Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support.en_US
dc.format.extent275 - 286en_US
dc.rightsTo be published as part of The International Conference on Probabilistic Graphical Models (PGM)
dc.titleA Progressive Explanation of Inference in ‘Hybrid’ Bayesian Networks for Supporting Clinical Decision Makingen_US
dc.typeConference Proceeding
pubs.notesNot knownen_US
pubs.publication-statusPublisheden_US
pubs.publisher-urlhttp://proceedings.mlr.press/v52/kyrimi16.htmlen_US
pubs.volume52en_US
dcterms.dateAccepted2016-07-04en_US


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