Fine-Tuned Transformers and Large Language Models for Entity Recognition in Complex Eligibility Criteria for Clinical Trials
Author:
Affiliation:
1. Roche Poland, Poland
2. Poznań University of Technology, Poland
Publisher
University of Gdańsk
Reference20 articles.
1. 1. Achiam, J., Adler, S., Agarwal, S., et al.: Gpt-4 technical report. arXiv:2303.08774 (2023)
2. 2. Alsentzer, E., Murphy, J.R., Boag, W., Weng, W.H., Jin, D., Naumann, T., McDermott, M.: Publicly available clinical bert embeddings. arXiv:1904.03323 (2019)
3. 3. Beltagy, I., et al.: Scibert: A pretrained language model for scientific text. arXiv:1903.10676 (2019)
4. 4. Dai, X., Karimi, S., Hachey, B., Paris, C.: An effective transition-based model for discontinuous ner. arXiv:2004.13454 (2020)
5. 5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv:1810.04805 (2018)
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