# Expert Opinion Extraction from a Biomedical Database

arXiv: https://arxiv.org/abs/1709.03270 · HTML: https://mlsanity.com/paper/1709.03270/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Sep 2017.

## Built on by (most cited first, 7 of 7)

- [Belief Evolution Network-based Probability Transformation and Fusion](https://mlsanity.com/paper/2110.03468/) (2021)
- [Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory](https://mlsanity.com/paper/2210.17330/) (2022)
- [BF-QC: Belief Functions on Quantum Circuits](https://mlsanity.com/paper/2107.03930/) (2021)
- [A geometric approach to conditioning belief functions](https://mlsanity.com/paper/2104.10651/) (2021)
- [Efficient exact computation of the conjunctive and disjunctive decompositions of D-S Theory for information fusion: Translation and extension](https://mlsanity.com/paper/2107.06329/) (2021)
- [Quantum Information Fusion and Correction under the Transferable Belief Model](https://mlsanity.com/paper/2410.08949/) (2024)
- [Evidential Information Fusion on Possibilistic Structure](https://mlsanity.com/paper/2605.17038/) (2026)

## Abstract (arXiv)

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.