MeSH Concept Relevance and Knowledge Evolution: A Data-driven Perspective
<|reference_start|>MeSH Concept Relevance and Knowledge Evolution: A Data-driven Perspective: The Medical Subject Headings (MeSH), one of the main knowledge organization systems in the biomedical domain, constantly evolves following the latest scientific discoveries in health and life sciences. Previous research focused on quantifying information in MeSH using its hierarchical structure. In this work, we propose a data-driven approach based on information theory and network analyses to quantify the relevance of MeSH concepts. Our approach leverages article annotations and their citation networks to compute informativeness, usefulness, disruptiveness, and influence of MeSH concepts over time. Using the the citation network and the MeSH hierarchy, different relevance aspects are computed, and an aggregation algorithm is used to propagate the relevance scores to the parent nodes. We evaluated our approach using changes in the terminology and showed that it effectively captures the evolution of MeSH concepts. At the first level of the hierarchy, the most relevant concept - Chemical and Drugs - had a decreasing trend (\textit{p}-value $< 0.01$), while at the second level, the most relevant concept - Neoplasms - had an increasing trend (\textit{p}-value $< 0.01$). We show that the mean relevance of evolving concepts is higher for concepts that remained unchanged (2.09E-03 \textit{vs.} 8.46E-04). Moreover, we validated the ability of our framework to characterize retracted articles and showed that concepts used to annotate retracted articles (mean relevance: 0.17) differ substantially from those used to annotate non-retracted ones (mean relevance: 0.15). The proposed framework provides an effective method to rank concept relevance and can be useful in maintaining evolving knowledge organization systems.<|reference_end|>
@article{copara2024mesh,
    title={MeSH Concept Relevance and Knowledge Evolution: A Data-driven
  Perspective},
    author={Jenny Copara, Nona Naderi, Gilles Falquet, Douglas Teodoro},
    journal={arXiv preprint arXiv:2406.18792},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.18792},
    primaryClass={cs.SI}
}