Literature DB >> 12031603

Probabilities for a probabilistic network: a case study in oesophageal cancer.

L C van der Gaag1, S Renooij, C L M Witteman, B M P Aleman, B G Taal.   

Abstract

With the help of two experts in gastrointestinal oncology from The Netherlands Cancer Institute, Antoni van Leeuwenhoekhuis, a decision-support system is being developed for patient-specific therapy selection for oesophageal cancer. The kernel of the system is a probabilistic network that describes the presentation characteristics of cancer of the oesophagus and the pathophysiological processes of invasion and metastasis. While the construction of the graphical structure of the network was relatively straightforward, probability elicitation with existing methods proved to be a major obstacle. To overcome this obstacle, we designed a new method for eliciting probabilities from experts that combines the ideas of transcribing probabilities as fragments of text and of using a scale with both numerical and verbal anchors for marking assessments. In this paper, we report experiences with our method in eliciting the probabilities required for the oesophagus network. The method allowed us to elicit many probabilities in reasonable time. To gain some insight in the quality of the probabilities obtained, we conducted a preliminary evaluation study of our network, using data from real patients. We found that for 85% of the patients, the network predicted the correct cancer stage.

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Year:  2002        PMID: 12031603     DOI: 10.1016/s0933-3657(02)00012-x

Source DB:  PubMed          Journal:  Artif Intell Med        ISSN: 0933-3657            Impact factor:   5.326


  7 in total

1.  Impact of precision of Bayesian network parameters on accuracy of medical diagnostic systems.

Authors:  Agnieszka Oniśko; Marek J Druzdzel
Journal:  Artif Intell Med       Date:  2013-03-05       Impact factor: 5.326

2.  Using bayesian networks to model hierarchical relationships in epidemiological studies.

Authors:  Georges Nguefack-Tsague
Journal:  Epidemiol Health       Date:  2011-06-17

3.  Medicine in words and numbers: a cross-sectional survey comparing probability assessment scales.

Authors:  Cilia L M Witteman; Silja Renooij; Pieter Koele
Journal:  BMC Med Inform Decis Mak       Date:  2007-06-11       Impact factor: 2.796

4.  Bayesian networks for clinical decision support in lung cancer care.

Authors:  M Berkan Sesen; Ann E Nicholson; Rene Banares-Alcantara; Timor Kadir; Michael Brady
Journal:  PLoS One       Date:  2013-12-06       Impact factor: 3.240

5.  From complex questionnaire and interviewing data to intelligent Bayesian network models for medical decision support.

Authors:  Anthony Costa Constantinou; Norman Fenton; William Marsh; Lukasz Radlinski
Journal:  Artif Intell Med       Date:  2016-01-16       Impact factor: 5.326

6.  Integrating Expert Knowledge with Data in Bayesian Networks: Preserving Data-Driven Expectations when the Expert Variables Remain Unobserved.

Authors:  Anthony Costa Constantinou; Norman Fenton; Martin Neil
Journal:  Expert Syst Appl       Date:  2016-03-18       Impact factor: 6.954

Review 7.  Artificial intelligence-assisted esophageal cancer management: Now and future.

Authors:  Yu-Hang Zhang; Lin-Jie Guo; Xiang-Lei Yuan; Bing Hu
Journal:  World J Gastroenterol       Date:  2020-09-21       Impact factor: 5.742

  7 in total

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