Expert Opinion Extraction from a Biomedical Database

Abstract : In this paper, we tackle the problem of extracting frequent opinions from uncertain databases. We introduce the foundation of an opinion mining approach with the definition of pattern and support measure. The support measure is derived from the commitment definition. A new algorithm called OpMiner that extracts the set of frequent opinions modelled as a mass functions is detailed. Finally, we apply our approach on a real-world biomedical database that stores opinions of experts to evaluate the reliability level of biomedical data. Performance analysis showed a better quality patterns for our proposed model in comparison with literature-based methods.
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Communication dans un congrès
Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Jul 2017, Lugano, Switzerland. Springer, 31 (LNCS 10369), pp.1 - 12, 2017, Proceedings of 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. 〈http://www2.idsia.ch/cms/isipta-ecsqaru/〉. 〈10.1016/S0888-613X(02)00066-X〉
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Dernière modification le : lundi 3 septembre 2018 - 13:33:04
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Ahmed Samet, Thomas Guyet, Benjamin Negrevergne, Tien-Tuan Dao, Tuan Nha Hoang, et al.. Expert Opinion Extraction from a Biomedical Database. Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty (ECSQARU), Jul 2017, Lugano, Switzerland. Springer, 31 (LNCS 10369), pp.1 - 12, 2017, Proceedings of 14th European Conference on Symbolic and Quantitative Approaches to Reasoning with Uncertainty. 〈http://www2.idsia.ch/cms/isipta-ecsqaru/〉. 〈10.1016/S0888-613X(02)00066-X〉. 〈hal-01584984〉

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