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Automatic Annotation of Clinicaltrials.gov Entities Using Large Language Models

  • 2025
  • OriginalPaper
  • Chapter
Published in:
Authors
Pouyan Nahed
Sepideh Farivar
Kazem Taghva
Published in
Proceedings of Tenth International Congress on Information and Communication Technology
Publisher
Springer Nature Singapore

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Abstract

This chapter explores the automatic annotation of clinical trial entities using large language models, focusing on the extraction of biomedical entities from the free-text eligibility criteria of ClinicalTrials.gov. The research introduces a novel dataset containing 497,812 documents with over 4.7 million sentences and 4.6 million entities, categorized into types such as diseases, chemicals, and interventions. The dataset employs pseudo-labeling techniques to generate nuanced annotations with confidence scores, enhancing the accuracy and reliability of entity recognition. Challenges encountered during annotation, such as label inconsistencies, were addressed through a structured mapping strategy to ensure uniformity. The dataset's extensive entity distribution and document-level analysis provide valuable insights for various downstream tasks, including NER, information extraction, and text classification. This work contributes significantly to the field of biomedical NLP by offering a publicly available resource that supports advancements in healthcare data processing and AI-driven solutions.
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next chapter Typology of Purpose of Generative Artificial Intelligence Use
Title
Automatic Annotation of Clinicaltrials.gov Entities Using Large Language Models
Authors
Pouyan Nahed
Sepideh Farivar
Kazem Taghva
Copyright Year
2025
Publisher
Springer Nature Singapore
Book
Proceedings of Tenth International Congress on Information and Communication Technology

Print ISBN: 978-981-9669-28-8

Electronic ISBN: 978-981-9669-29-5

Copyright Year: 2025

DOI
https://doi.org/10.1007/978-981-96-6929-5_35
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