Expert Opinion Extraction from a Biomedical Database

TL;DR

This paper presents OpMiner, a novel algorithm for extracting frequent expert opinions from uncertain biomedical databases, improving pattern quality over existing methods.

Contribution

Introduction of a new opinion mining framework with a support measure based on commitment, and the development of OpMiner for extracting frequent opinions modeled as mass functions.

Findings

OpMiner outperforms literature-based methods in pattern quality.

Application on biomedical data demonstrates effectiveness in assessing data reliability.

Proposed approach effectively handles uncertainty in expert opinions.

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.