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[Preprint]. 2025 Jun 3:arXiv:2506.03321v1.

Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models

Victor H Cid  1 , James Mork  1
Affiliations

Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models

Victor H Cid et al. ArXiv. .

Abstract

We investigated the feasibility of predicting Medical Subject Headings (MeSH) Publication Types (PTs) from MEDLINE citation metadata using pre-trained Transformer-based models BERT and DistilBERT. This study addresses limitations in the current automated indexing process, which relies on legacy NLP algorithms. We evaluated monolithic multi-label classifiers and binary classifier ensembles to enhance the retrieval of biomedical literature. Results demonstrate the potential of Transformer models to significantly improve PT tagging accuracy, paving the way for scalable, efficient biomedical indexing.

Keywords: MEDLINE; Machine Learning; MeSH Publication Types; Natural Language Processing; Pre-trained Foundation Models.

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Figures

Figure 1:
Figure 1:
MDELINE Publication Type tag correlations.
Figure 2:
Figure 2:
Monolithic multilabel classifier architecture.
Figure 3:
Figure 3:
Ensemble of binary classifiers architecture, with example PT class recall scores.
See this image and copyright information in PMC

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References

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