A Pilot Study on Fine-tuning Named Entity Recognition for Clinical Tag Extraction Using Pretrained Language Models: The Tut-All Experience
Year: 2025
Type: conference-paper
Abstract: The standard procedure to extract data from medical records is still a laborious process where research analysts examine a wide range of information sources. Hence, there is a strong need to improve automatic data extraction techniques. This study performed fine-tuning of the pretrained language models (PLMs)—BioElectra, PubMedBert, BioLinkBert, and Clinical Longformer—for named entity recognition (NER) in clinical texts. The clinical texts were extracted from records using the Tut-All software and were comprised of abstracts and methods sections. For performance analysis the mean of the F1-score for each named entity tag was used across different hyperparameter optimizations. PubMedBert achieved the best performance results with a mean F1-score of 0.638 and an output range of 0.3. This performance was achieved after training with a learning rate and a weight decay of 5×10-5 and 3×10-3 respectively, employing default HuggingFace hyperparameters. Notably, PubMedBert exhibited superior performance on minority entity types, addressing a key challenge in real-world data applications. The optimized model not only expedites information retrieval, but also enhances the overall quality of extracted data, marking a valuable contribution to the data extraction data from medical records.
Authors: Gbadamosi, Adeyemi Victor, Montero, Alberto, Deutsch, Martin, Sander, Nick, Cashmore, Claire, Sousa, Eva
Institutions: University of Hull, Hull York Medical School
Language: English
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W6949555043
FWCI:
Cites:
Cited by:
Related to: