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A long-running goal of the clinical NLP community is the extraction of important variables trapped in clinical notes.
Language models are few-shot learners
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Publicly available clinical bert embeddings
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Simple bert models for relation extraction and semantic role labeling
Peng Shi and Jimmy Lin. 2019 · 1904
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Entity-relation extraction as multi-turn question answering
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A unified mrc framework for named entity recognition
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Huggingface’s transformers: State-of-the-art natural language processing
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A simple algorithm for identifying negated findings and diseases in discharge summaries
Wendy W Chapman, Will Bridewell, Paul Hanbury, Gregory F Cooper, and Bruce G Buchanan. 2001 · 2001
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Developing a test collection for biomedical word sense disambiguation
Marc Weeber, James G Mork, and Alan R Aronson. 2001 · 2001
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The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider. 2004 · 2004
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Bert-xml: Large scale automated icd coding using bert pretraining
Zachariah Zhang, Jingshu Liu, and Narges Razavian. 2020 · 2006
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Abbreviations and acronyms in healthcare: when shorter isn’t sweeter
Ivy Fenton Kuhn. 2007 · 2007
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Frontiers of biomedical text mining: current progress
Pierre Zweigenbaum, Dina Demner-Fushman, Hong Yu, and Kevin B Cohen. 2007 · 2007
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Seventy-five trials and eleven systematic reviews a day: how will we ever keep up?
Hilda Bastian, Paul Glasziou, and Iain Chalmers. 2010 · 2010
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Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2)
Shawn N Murphy, Griffin Weber, Michael Mendis, Vivian Gainer, Henry C Chueh, Susanne Churchill, and Isaac Kohane. 2010 · 2010
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Learning to ignore: Long document coreference with bounded memory neural networks
Shubham Toshniwal, Sam Wiseman, Allyson Ettinger, Karen Livescu, and Kevin Gimpel. 2020 · 2010
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Extracting medication information from clinical text
Özlem Uzuner, Imre Solti, and Eithon Cadag. 2010 · 2010
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A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
Min Jiang, Yukun Chen, Mei Liu, S Trent Rosenbloom, Subramani Mani, Joshua C Denny, and Hua Xu. 2011 · 2011
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Coreference resolution: A review of general methodologies and applications in the clinical domain
Jiaping Zheng, Wendy W Chapman, Rebecca S Crowley, and Guergana K Savova. 2011 · 2011
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Evaluating the state of the art in coreference resolution for electronic medical records
Ozlem Uzuner, Andreea Bodnari, Shuying Shen, Tyler Forbush, John Pestian, and Brett R South. 2012 · 2012
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A statistical relational learning approach to identifying evidence based medicine categories
Mathias Verbeke, Vincent Van Asch, Roser Morante, Paolo Frasconi, Walter Daelemans, and Luc De Raedt. 2012 · 2012
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Clinical corpus annotation: challenges and strategies
Fei Xia and Meliha Yetisgen-Yildiz. 2012 · 2012
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Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean. 2013 · 2013
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Normalizing acronyms and abbreviations to aid patient understanding of clinical texts: Share/clef ehealth challenge 2013, task 2
Danielle L Mowery, Brett R South, Lee Christensen, Jianwei Leng, Laura-Maria Peltonen, Sanna Salanterä, Hanna Suominen, David Martinez, Sumithra Velupillai, Noémie Elhadad, et al. 2016 · 2013
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A sense inventory for clinical abbreviations and acronyms created using clinical notes and medical dictionary resources
Sungrim Moon, Serguei Pakhomov, Nathan Liu, James O Ryan, and Genevieve B Melton. 2014 · 2014
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Medxn: an open source medication extraction and normalization tool for clinical text
Sunghwan Sohn, Cheryl Clark, Scott R Halgrim, Sean P Murphy, Christopher G Chute, and Hongfang Liu. 2014 · 2014
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Clinical abbreviation disambiguation using neural word embeddings
Yonghui Wu, Jun Xu, Yaoyun Zhang, and Hua Xu. 2015 · 2015
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Leveraging coreference to identify arms in medical abstracts: An experimental study
Elisa Ferracane, Iain Marshall, Byron C Wallace, and Katrin Erk. 2016 · 2016
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Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, Li-wei H Lehman, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark. 2016 · 2016
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Assessing the corpus size vs. similarity trade-off for word embeddings in clinical nlp
Kirk Roberts. 2016 · 2016
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Deep learning in clinical natural language processing: a methodical review
Stephen Wu, Kirk Roberts, Surabhi Datta, Jingcheng Du, Zongcheng Ji, Yuqi Si, Sarvesh Soni, Qiong Wang, Qiang Wei, Yang Xiang, et al. 2020 · 2020
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Intimate partner violence and injury prediction from radiology reports
Irene Y Chen, Emily Alsentzer, Hyesun Park, Richard Thomas, Babina Gosangi, Rahul Gujrathi, and Bharti Khurana. 2020 · 2021
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Template-based named entity recognition using bart
Leyang Cui, Yu Wu, Jian Liu, Sen Yang, and Yue Zhang. 2021 · 2021
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Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021 · 2021
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Extracting structured data from physician-patient conversations by predicting noteworthy utterances
Kundan Krishna, Amy Pavel, Benjamin Schloss, Jeffrey P Bigham, and Zachary C Lipton. 2021 · 2021
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Kenton Lee, Luheng He, Mike Lewis, and Luke Zettlemoyer. 2017 · 2017
Cited alongside, same era.
Snorkel: Rapid training data creation with weak supervision
Alexander Ratner, Stephen H Bach, Henry Ehrenberg, Jason Fries, Sen Wu, and Christopher Ré. 2017 · 2017
Cited alongside, same era.
Construction of the literature graph in semantic scholar
Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, Iz Beltagy, Miles Crawford, Doug Downey, Jason Dunkelberger, Ahmed Elgohary, Sergey Feldman, Vu Ha, et al. 2018 · 2018
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Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
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2018 n2c2 shared task on adverse drug events and medication extraction in electronic health records
Sam Henry, Kevin Buchan, Michele Filannino, Amber Stubbs, and Ozlem Uzuner. 2020 · 2018
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter. 2018 · 2018
Cited alongside, same era.
Clinical natural language processing in languages other than english: opportunities and challenges
Aurélie Névéol, Hercules Dalianis, Sumithra Velupillai, Guergana Savova, and Pierre Zweigenbaum. 2018 · 2018
Cited alongside, same era.
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Assessing the impact of automated suggestions on decision making: Domain experts mediate model errors but take less initiative
Ariel Levy, Monica Agrawal, Arvind Satyanarayan, and David Sontag. 2021 · 2021
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Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021 · 2021
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Reframing instructional prompts to gptk’s language
Swaroop Mishra, Daniel Khashabi, Chitta Baral, Yejin Choi, and Hannaneh Hajishirzi. 2021 · 2021
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Gpt-3 models are poor few-shot learners in the biomedical domain
Milad Moradi, Kathrin Blagec, Florian Haberl, and Matthias Samwald. 2021 · 2021
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Multitask prompted training enables zero-shot task generalization
Victor Sanh, Albert Webson, Colin Raffel, Stephen H Bach, Lintang Sutawika, Zaid Alyafeai, Antoine Chaffin, Arnaud Stiegler, Teven Le Scao, Arun Raja, et al. 2021 · 2021
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Exploiting cloze-questions for few-shot text classification and natural language inference
Timo Schick and Hinrich Schütze. 2021 · 2021
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Automatically disambiguating medical acronyms with ontology-aware deep learning
Marta Skreta, Aryan Arbabi, Jixuan Wang, Erik Drysdale, Jacob Kelly, Devin Singh, and Michael Brudno. 2021 · 2021
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On generalization in coreference resolution
Shubham Toshniwal, Patrick Xia, Sam Wiseman, Karen Livescu, and Kevin Gimpel. 2021 · 2021
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Want to reduce labeling cost? gpt-3 can help
Shuohang Wang, Yang Liu, Yichong Xu, Chenguang Zhu, and Michael Zeng. 2021 · 2021
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Finetuned language models are zero-shot learners
Jason Wei, Maarten Bosma, Vincent Y. Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V. Le. 2021 · 2021
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Wrench: A comprehensive benchmark for weak supervision
Jieyu Zhang, Yue Yu, Yinghao Li, Yujing Wang, Yaming Yang, Mao Yang, and Alexander Ratner. 2021 · 2021
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Thinking about gpt-3 in-context learning for biomedical ie? think again
Bernal Jiménez Gutiérrez, Nikolas McNeal, Clay Washington, You Chen, Lang Li, Huan Sun, and Yu Su. 2022 · 2022
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Instruction induction: From few examples to natural language task descriptions
Or Honovich, Uri Shaham, Samuel R Bowman, and Omer Levy. 2022 · 2022
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Rethinking the role of demonstrations: What makes in-context learning work?
Sewon Min, Xinxi Lyu, Ari Holtzman, Mikel Artetxe, Mike Lewis, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2022 · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al. 2022 · 2022
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Language models in the loop: Incorporating prompting into weak supervision
Ryan Smith, Jason A Fries, Braden Hancock, and Stephen H Bach. 2022 · 2022
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Ai chains: Transparent and controllable human-ai interaction by chaining large language model prompts
Tongshuang Wu, Michael Terry, and Carrie Jun Cai. 2022 · 2022
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What gpt knows about who is who
Xiaohan Yang, Eduardo Peynetti, Vasco Meerman, and Chris Tanner. 2022 · 2022
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Opt: Open pre-trained transformer language models
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