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

• URL: http://arxiv.org/abs/2506.03321v1
• Date: Tue, 03 Jun 2025 19:06:51 GMT
• Title: Enhancing Automatic PT Tagging for MEDLINE Citations Using Transformer-Based Models
• Authors: Victor H. Cid, James Mork,
• Abstract summary: We investigated the feasibility of predicting Medical Subject Headings (PTs) from MEDLINE citation metadata using pre-trained Transformer-based models BERT and DistilBERT.<n>Results demonstrate the potential of Transformer models to significantly improve PT tagging accuracy, paving the way for scalable, efficient biomedical indexing.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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.

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