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A large language model for electronic health records | npj Digital ...

A large language model for electronic health records | npj Digital ... unknown

  1. Adoption of Electronic Health Record Systems among U.S. Non-Federal Acute Care Hospitals: 2008–2015. ONC Data Brief. https://www.healthit.gov/sites/default/files/briefs/2015_hospital_adoption_db_v17.pdf (2016).

Adler-Milstein, J. et al. Electronic health record adoption in US hospitals: the emergence of a digital ‘advanced use’ divide. J. Am. Med. Inform. Assoc. 24, 1142–1148 (2017).

Article                    Google Scholar

Bush, R. A., Kuelbs, C. L., Ryu, J., Jian, W. & Chiang, G. J. Structured data entry in the electronic medical record: perspectives of pediatric specialty physicians and surgeons. J. Med. Syst. 41, 1–8 (2017).

Article                    Google Scholar

Meystre, S. M., Savova, G. K., Kipper-Schuler, K. C. & Hurdle, J. F. Extracting information from textual documents in the electronic health record: a review of recent research. Yearb. Med. Inform. 17, 128–144 (2008).

Article                    Google Scholar

Liang, H. et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat. Med. 25, 433–438 (2019).

Article CAS                    Google Scholar

Yang, J. et al. Assessing the prognostic significance of tumor-infiltrating lymphocytes in patients with melanoma using pathologic features identified by natural language processing. JAMA Netw. Open 4, e2126337 (2021).

Article                    Google Scholar

Nadkarni, P. M., Ohno-Machado, L. & Chapman, W. W. Natural language processing: an introduction. J. Am. Med. Inform. Assoc. 18, 544–551 (2011).

Article                    Google Scholar

LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

Article CAS                    Google Scholar

Collobert, R. et al. Natural language processing (almost) from scratch. J. Mach. Learn Res. 12, 2493–2537 (2011).

Google Scholar

  1. Lample, G., Ballesteros, M., Subramanian, S., Kawakami, K. & Dyer, C. Neural architectures for named entity recognition. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 260–270 (2016).

Lee, J. et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics. 36, 1234–1240 (2020).

CAS                    Google Scholar

  1. Vaswani, A. et al. Attention is All you Need. Advances in Neural Information Processing Systems. 30 (2017).
  2. Wang, A. et al. GLUE: A multi-task benchmark and analysis platform for natural language understanding. Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP. 353–355 (2018).
  3. Wang, A. et al. SuperGLUE: a stickier benchmark for general-purpose language understanding systems. Advances in neural information processing systems. 32 (2019).

Qiu, X. et al. Pre-trained models for natural language processing: a survey. Science China Technological Sciences. 63, 1872–1897 (2020).

Article                    Google Scholar

Tay, Y., Dehghani, M., Bahri, D. & Metzler, D. Efficient transformers: a survey. ACM Computing Surveys. 55, 1–28 (2020).

Article                    Google Scholar

  1. Yu, J., Bohnet, B. & Poesio, M. Named entity recognition as dependency parsing. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 6470–6476 (2020).
  2. Yamada, I., Asai, A., Shindo, H., Takeda, H. & Matsumoto, Y. LUKE: deep contextualized entity representations with entity-aware self-attention. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 6442–6454 (2020).
  3. Li, X. et al. Dice loss for data-imbalanced NLP tasks. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 465–476 (2020).

Xu, B., Wang, Q., Lyu, Y., Zhu, Y. & Mao, Z. Entity structure within and throughout: modeling mention dependencies for document-level relation extraction. Proceedings of the AAAI Conference on Artificial Intelligence 35, 14149–14157 (2021).

Article                    Google Scholar

  1. Ye, D., Lin, Y. & Sun, M. Pack together: entity and relation extraction with levitated marker. Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics. 1, 4904–4917 (2021).
  2. Cohen, A. D., Rosenman, S. & Goldberg, Y. Relation classification as two-way span-prediction. ArXiv arXiv:2010.04829 (2021).
  3. Lyu, S. & Chen, H. Relation classification with entity type restriction. Findings of the Association for Computational Linguistics: ACL-IJCNLP. 390–395 (2021).
  4. Wang, J. & Lu, W. Two are better than one: joint entity and relation extraction with table-sequence encoders. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 1706–1721 (2020).
  5. Jiang, H. et al. SMART: Robust and efficient fine-tuning for pre-trained natural language models through principled regularized optimization. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2177–2190 (2020).
  6. Yang, Z. et al. XLNet: Generalized Autoregressive Pretraining for Language Understanding. Proceedings of the 33rd International Conference on Neural Information Processing Systems. 5753–5763 (2019).

Raffel, C. et al. Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21, 1–67 (2019).

Google Scholar

  1. Lan, Z.-Z. et al. ALBERT: a lite BERT for self-supervised learning of language representations. ArXiv arXiv:1909.11942 (2019).
  2. Wang, S., Fang, H., Khabsa, M., Mao, H. & Ma, H. Entailment as Few-Shot Learner. ArXiv arXiv:2104.14690 (2021).
  3. Zhang, Z. et al. Semantics-aware BERT for language understanding. Proceedings of the AAAI Conference on Artificial Intelligence. 34, 9628-9635 (2020).
  4. Zhang, Z., Yang, J. & Zhao, H. Retrospective reader for machine reading comprehension. Proceedings of the AAAI Conference on Artificial Intelligence. 35, 14506-14514 (2021).
  5. Garg, S., Vu, T. & Moschitti, A. TANDA: transfer and adapt pre-trained transformer models for answer sentence selection. Proceedings of the AAAI Conference on Artificial Intelligence. 34, 7780-7788 (2020).
  6. Bommasani, R. et al. On the opportunities and risks of foundation models. ArXiv arXiv:2108.07258 (2021).

Floridi, L. & Chiriatti, M. GPT-3: its nature, scope, limits, and consequences. Minds Mach 30, 681–694 (2020).

Article                    Google Scholar

Gu, Y. et al. Domain-specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthc. 3, 1–23 (2022).

Article                    Google Scholar

  1. Shin, H.-C. et al. BioMegatron: larger biomedical domain language model. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 4700–4706 (2020).
  2. Alsentzer, E. et al. Publicly Available Clinical BERT Embeddings. in Proc. 2nd Clinical Natural Language Processing Workshop 72–78 (2019).

Johnson, A. E. W. et al. MIMIC-III, a freely accessible critical care database. Sci. Data 3, 160035 (2016).

Article CAS                    Google Scholar

Uzuner, Ö., South, B. R., Shen, S. & DuVall, S. L. 2010 i2b2/VA challenge on concepts, assertions, and relations in clinical text. J. Am. Med. Inform. Assoc. 18, 552–556 (2011).

Article                    Google Scholar

Sun, W., Rumshisky, A. & Uzuner, O. Evaluating temporal relations in clinical text: 2012 i2b2 Challenge. J. Am. Med. Inform. Assoc. 20, 806–813 (2013).

Article                    Google Scholar

Yang, X. et al. Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting. J. Am. Med. Inform. Assoc. 27, 65–72 (2020).

Article                    Google Scholar

Yang, X. et al. A study of deep learning methods for de-identification of clinical notes in cross-institute settings. BMC Med. Inform. Decis. Mak. 19, 232 (2019).

Article                    Google Scholar

  1. Shoeybi, M. et al. Megatron-LM: training multi-billion parameter language models using model parallelism. ArXiv arXiv:1909.08053 (2020).

Levine, Y., Wies, N., Sharir, O., Bata, H. & Shashua, A. Limits to depth efficiencies of self-attention. Advances in Neural Information Processing Systems 33, 22640–22651 (2020).

Google Scholar

  1. Sennrich, R., Haddow, B. & Birch, A. Neural Machine Translation of Rare Words with Subword Units. in Proc. 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) 1715–1725 (Association for Computational Linguistics, 2016).
  2. Devlin, J., Chang, M.-W., Lee, K. & Toutanova, K. BERT: pre-training of deep bidirectional transformers for language understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 4171–4186 (2019).

Wu, Y., Xu, J., Jiang, M., Zhang, Y. & Xu, H. A study of neural word embeddings for named entity recognition in clinical text. Amia. Annu. Symp. Proc. 2015, 1326–1333 (2015).

Google Scholar

Soysal, E. et al. CLAMP—a toolkit for efficiently building customized clinical natural language processing pipelines. J. Am. Med. Inform. Assoc. 25, 331–336 (2018).

Article                    Google Scholar

Wu, Y., Jiang, M., Lei, J. & Xu, H. Named entity recognition in chinese clinical text using deep neural network. Stud. Health Technol. Inform. 216, 624–628 (2015).

Google Scholar

  1. Wu, Y. et al. Combine factual medical knowledge and distributed word representation to improve clinical named entity recognition. in AMIA Annual Symposium Proceedings vol. 2018, 1110 (American Medical Informatics Association, 2018).

Yang, X. et al. Identifying relations of medications with adverse drug events using recurrent convolutional neural networks and gradient boosting. J. Am. Med. Inform. Assoc. 27, 65–72 (2020).

Article                    Google Scholar

  1. Kumar, S. A survey of deep learning methods for relation extraction. ArXiv arXiv:1705.03645 (2017).

Lv, X., Guan, Y., Yang, J. & Wu, J. Clinical relation extraction with deep learning. Int. J. Hybrid. Inf. Technol. 9, 237–248 (2016).

Google Scholar

Wei, Q. et al. Relation extraction from clinical narratives using pre-trained language models. Amia. Annu. Symp. Proc. 2019, 1236–1245 (2020).

Google Scholar

Guan, H. & Devarakonda, M. Leveraging contextual information in extracting long distance relations from clinical notes. Amia. Annu. Symp. Proc. 2019, 1051–1060 (2020).

Google Scholar

Alimova, I. & Tutubalina, E. Multiple features for clinical relation extraction: a machine learning approach. J. Biomed. Inform. 103, 103382 (2020).

Article                    Google Scholar

  1. Mahendran, D. & McInnes, B. T. Extracting adverse drug events from clinical notes. AMIA Summits on Translational Science Proceedings. 420–429 (2021).

Yang, X., Zhang, H., He, X., Bian, J. & Wu, Y. Extracting family history of patients from clinical narratives: exploring an end-to-end solution with deep learning models. JMIR Med. Inform. 8, e22982 (2020).

Article                    Google Scholar

  1. Yang, X., Yu, Z., Guo, Y., Bian, J. & Wu, Y. Clinical Relation Extraction Using Transformer-based Models. ArXiv. arXiv:2107.08957 (2021).
  2. Cer, D., Diab, M., Agirre, E., Lopez-Gazpio, I. & Specia, L. Semeval-2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation. Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017). 1–14 (2017).
  3. Farouk, M. Measuring sentences similarity: a survey. ArXiv arXiv:1910.03940 (2019).

Ramaprabha, J., Das, S. & Mukerjee, P. Survey on sentence similarity evaluation using deep learning. J. Phys. Conf. Ser. 1000, 012070 (2018).

Article                    Google Scholar

Gomaa, W. H. & Fahmy, A. A survey of text similarity approaches. International journal of Computer Applications 68, 13–18 (2013).

Article                    Google Scholar

Wang, Y. et al. MedSTS: a resource for clinical semantic textual similarity. Lang. Resour. Eval. 54, 57–72 (2020).

Article                    Google Scholar

  1. Rastegar-Mojarad, M. et al. BioCreative/OHNLP Challenge 2018. in Proc. 2018 ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics 575–575 (ACM, 2018).

Wang, Y. et al. Overview of the 2019 n2c2/OHNLP track on clinical semantic textual similarity. JMIR Med. Inform. 8, e23375 (2020).

Article                    Google Scholar

Mahajan, D. et al. Identification of semantically similar sentences in clinical notes: iterative intermediate training using multi-task learning. JMIR Med. Inform. 8, e22508 (2020).

Article                    Google Scholar

  1. Dagan, I., Glickman, O. & Magnini, B. in Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment (eds. Quiñonero-Candela, J., Dagan, I., Magnini, B. & d’Alché-Buc, F.) 177–190 (Springer Berlin Heidelberg, 2006).
  2. Williams, A., Nangia, N. & Bowman, S. R. A broad-coverage challenge corpus for sentence understanding through inference. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 1, 1112–1122 (2018).
  3. Bowman, S. R., Angeli, G., Potts, C. & Manning, C. D. A large annotated corpus for learning natural language inference. Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. 632–642 (2015).

Shivade, C. MedNLI—a natural language inference dataset for the clinical domain. PhysioNet https://doi.org/10.13026/C2RS98 (2017).

Article                    Google Scholar

  1. Conneau, A., Kiela, D., Schwenk, H., Barrault, L. & Bordes, A. Supervised learning of universal sentence representations from natural language inference data. Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing. 670–680 (2017).
  2. Rajpurkar, P., Zhang, J., Lopyrev, K. & Liang, P. SQuAD: 100,000+ questions for machine comprehension of text. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2383–2392 (2016).

Rajpurkar, P., Jia, R. & Liang, P. Know what you don’t know: unanswerable questions for SQuAD. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics 2, 784–789 (2018).

Google Scholar

  1. Zhu, M., Ahuja, A., Juan, D.-C., Wei, W. & Reddy, C. K. Question Answering with Long Multiple-Span Answers. in Findings of the Association for Computational Linguistics: EMNLP 2020 3840–3849 (Association for Computational Linguistics, 2020).

Ben Abacha, A. & Demner-Fushman, D. A question-entailment approach to question answering. BMC Bioinforma 20, 511 (2019).

Article                    Google Scholar

  1. Pampari, A., Raghavan, P., Liang, J. & Peng, J. emrQA: a large corpus for question answering on electronic medical records. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2357–2368 (2018).
  2. Yue, X., Gutierrez, B. J. & Sun, H. Clinical reading comprehension: a thorough analysis of the emrQA dataset. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 4474–4486 (2020).