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As part of the large number of scientific articles being published every year, the publication rate of biomedical literature has been increasing.
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O. Bodenreider, “The unified medical language system (umls): integrating biomedical terminology,” Nucleic acids research , vol. 32, no. suppl_1, pp. D267–D270, 2004
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2010
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R. I. Doğan, R. Leaman, and Z. Lu, “Ncbi disease corpus: a resource for disease name recognition and concept normalization,” Journal of biomedical informatics , vol. 47, pp. 1–10, 2014
2014
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W. Shen, J. Wang, and J. Han, “Entity linking with a knowledge base: Issues, techniques, and solutions,” IEEE Transactions on Knowledge and Data Engineering , vol. 27, no. 2, pp. 443–460, 2014
2014
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2014
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2014
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2014
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2015
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J. G. Zheng, D. Howsmon, B. Zhang, J. Hahn, D. McGuinness, J. Hendler, and H. Ji, “Entity linking for biomedical literature,” BMC medical informatics and decision making , vol. 15, no. 1, pp. 1–9, 2015
2015
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S. Banerjee, P. Mitra, and K. Sugiyama, “Multi-document abstractive summarization using ilp based multi-sentence compression,” in Twenty-Fourth International Joint Conference on Artificial Intelligence , 2015
2015
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2015
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S. Chopra, M. Auli, and A. M. Rush, “Abstractive sentence summarization with attentive recurrent neural networks,” in Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , 2016, pp. 93–98
2016
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J. Li, Y. Sun, R. J. Johnson, D. Sciaky, C.-H. Wei, R. Leaman, A. P. Davis, C. J. Mattingly, T. C. Wiegers, and Z. Lu, “Biocreative v cdr task corpus: a resource for chemical disease relation extraction,” Database , vol. 2016, 2016
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J. Hirsch, G. Nicola, G. McGinty, R. Liu, R. Barr, M. Chittle, and L. Manchikanti, “Icd-10: history and context,” American Journal of Neuroradiology , vol. 37, no. 4, pp. 596–599, 2016
2016
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F. Schulze and M. Neves, “Entity-supported summarization of biomedical abstracts,” in Proceedings of the Fifth Workshop on Building and Evaluating Resources for Biomedical Text Mining (BioTxtM2016) , 2016, pp. 40–49
2016
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2016
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2017
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Z. Zhao, Z. Yang, L. Luo, L. Wang, Y. Zhang, H. Lin, and J. Wang, “Disease named entity recognition from biomedical literature using a novel convolutional neural network,” BMC medical genomics , vol. 10, no. 5, pp. 75–83, 2017
2017
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A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in neural information processing systems , 2017, pp. 5998–6008
2017
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2017
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2020
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2018
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2019
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2020
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2020
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M. Afzal, F. Alam, K. M. Malik, and G. M. Malik, “Clinical context–aware biomedical text summarization using deep neural network: Model development and validation,” Journal of medical Internet research , vol. 22, no. 10, p. e19810, 2020
2020
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J. Lee, W. Yoon, S. Kim, D. Kim, S. Kim, C. H. So, and J. Kang, “Biobert: a pre-trained biomedical language representation model for biomedical text mining,” Bioinformatics , vol. 36, no. 4, pp. 1234–1240, 2020
2020
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M. Zaheer, G. Guruganesh, K. A. Dubey, J. Ainslie, C. Alberti, S. Ontanon, P. Pham, A. Ravula, Q. Wang, L. Yang et al. , “Big bird: Transformers for longer sequences.” in NeurIPS , 2020
2020
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V. Kocaman and D. Talby, “Biomedical named entity recognition at scale,” in International Conference on Pattern Recognition . Springer, 2021, pp. 635–646
2021
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H. Zhou, W. Ren, G. Liu, B. Su, and W. Lu, “Entity-aware abstractive multi-document summarization,” in Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 , 2021, pp. 351–362
2021
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J. E. Harrison, S. Weber, R. Jakob, and C. G. Chute, “Icd-11: an international classification of diseases for the twenty-first century,” BMC medical informatics and decision making , vol. 21, no. 6, pp. 1–10, 2021
2021
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2021
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G. Manas, V. Aribandi, U. Kursuncu, A. Alambo, V. L. Shalin, K. Thirunarayan, J. Beich, M. Narasimhan, A. Sheth et al. , “Knowledge-infused abstractive summarization of clinical diagnostic interviews: Framework development study,” JMIR Mental Health , vol. 8, no. 5, p. e20865, 2021
2021
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2021
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L. Jean-Baptiste, “Using medical terminologies with pymedtermino and umls,” in Ontologies with Python . Springer, 2021, pp. 207–239
2021
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D. Singh, S. Reddy, W. Hamilton, C. Dyer, and D. Yogatama, “End-to-end training of multi-document reader and retriever for open-domain question answering,” Advances in Neural Information Processing Systems , vol. 34, 2021
2021
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