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In this study, we aim to address the task of assertion detection when extracting medical concepts from clinical notes, a key process in clinical natural language processing (NLP).
1911
Earlier work this paper cites.
W. W. Chapman, W. Bridewell, P. Hanbury, G. F. Cooper, and B. G. Buchanan, “A simple algorithm for identifying negated findings and diseases in discharge summaries,” Journal of biomedical informatics , vol. 34, no. 5, pp. 301–310, 2001
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H. Harkema, J. N. Dowling, T. Thornblade, and W. W. Chapman, “Context: an algorithm for determining negation, experiencer, and temporal status from clinical reports,” Journal of biomedical informatics , vol. 42, no. 5, pp. 839–851, 2009
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G. K. Savova, J. J. Masanz, P. V. Ogren, J. Zheng, S. Sohn, K. C. Kipper-Schuler, and C. G. Chute, “Mayo clinical text analysis and knowledge extraction system (ctakes): architecture, component evaluation and applications,” Journal of the American Medical Informatics Association , vol. 17, no. 5, pp. 507–513, 2010
2010
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Ö. Uzuner, B. R. South, S. Shen, and S. L. DuVall, “2010 i2b2/va challenge on concepts, assertions, and relations in clinical text,” Journal of the American Medical Informatics Association , vol. 18, no. 5, pp. 552–556, 2011
2011
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H. Liu, S. J. Bielinski, S. Sohn, S. Murphy, K. B. Wagholikar, S. R. Jonnalagadda, K. Ravikumar, S. T. Wu, I. J. Kullo, and C. G. Chute, “An information extraction framework for cohort identification using electronic health records,” AMIA Summits on Translational Science Proceedings , vol. 2013, p. 149, 2013
2013
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Z. Qian, P. Li, Q. Zhu, G. Zhou, Z. Luo, and W. Luo, “Speculation and negation scope detection via convolutional neural networks,” in Proceedings of the 2016 conference on empirical methods in natural language processing , 2016, pp. 815–825
2016
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L. R. Kline, N. Collop, and G. Finlay, “Clinical presentation and diagnosis of obstructive sleep apnea in adults,” Uptodate. com [Internet] , 2017
2017
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E. Sergeeva, H. Zhu, P. Prinsen, and A. Tahmasebi, “Negation scope detection in clinical notes and scientific abstracts: a feature-enriched lstm-based approach,” AMIA Summits on Translational Science Proceedings , vol. 2019, p. 212, 2019
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T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” 2020
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H. Eyre, A. B. Chapman, K. S. Peterson, J. Shi, P. R. Alba, M. M. Jones, T. L. Box, S. L. DuVall, and O. V. Patterson, “Launching into clinical space with medspacy: a new clinical text processing toolkit in python,” in AMIA Annual Symposium Proceedings , vol. 2021. American Medical Informatics Association, 2021, p. 438
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2023
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S. Yao, D. Yu, J. Zhao, I. Shafran, T. L. Griffiths, Y. Cao, and K. Narasimhan, “Tree of thoughts: Deliberate problem solving with large language models,” 2023
2023
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J. Wei, X. Wang, D. Schuurmans, M. Bosma, B. Ichter, F. Xia, E. Chi, Q. Le, and D. Zhou, “Chain-of-thought prompting elicits reasoning in large language models,” 2023
2023
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X. Wang, J. Wei, D. Schuurmans, Q. Le, E. Chi, S. Narang, A. Chowdhery, and D. Zhou, “Self-consistency improves chain of thought reasoning in language models,” 2023
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E. J. Hu, Y. Shen, P. Wallis, Z. Allen-Zhu, Y. Li, S. Wang, L. Wang, and W. Chen, “Lora: Low-rank adaptation of large language models,” 2021
2021
Cited alongside, same era.
B. van Aken, I. Trajanovska, A. Siu, M. Mayrdorfer, K. Budde, and A. Loeser, “Assertion detection in clinical notes: Medical language models to the rescue?” in Proceedings of the Second Workshop on Natural Language Processing for Medical Conversations , C. Shivade, R. Gangadharaiah, S. Gella, S. Konam, S. Yuan, Y. Zhang, P. Bhatia, and B. Wallace, Eds. Online: Association for Computational Linguistics, Jun. 2021, pp. 35–40. [Online]. Available: https://aclanthology.org/2021.nlpmc-1.5
2021
Cited alongside, same era.
S. Wang, L. Tang, A. Majety, J. F. Rousseau, G. Shih, Y. Ding, and Y. Peng, “Trustworthy assertion classification through prompting,” Journal of biomedical informatics , vol. 132, p. 104139, 2022
2022
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2022
Cited alongside, same era.
S. Liu, A. Wen, L. Wang, H. He, S. Fu, R. Miller, A. Williams, D. Harris, R. Kavuluru, M. Liu et al. , “An open natural language processing (nlp) framework for ehr-based clinical research: a case demonstration using the national covid cohort collaborative (n3c),” Journal of the American Medical Informatics Association , vol. 30, no. 12, pp. 2036–2040, 2023
2023
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P. M. Arachchige and S. Arosh, “Large language models (llm) and chatgpt: a medical student perspective,” European Journal of Nuclear Medicine and Molecular Imaging , vol. 50, no. 8, pp. 2248–2249, 2023
2023
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2023
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S. Sivarajkumar, M. Kelley, A. Samolyk-Mazzanti, S. Visweswaran, and Y. Wang, “An empirical evaluation of prompting strategies for large language models in zero-shot clinical natural language processing,” 2023
2023
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