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Neural network-based representations ("embeddings") have dramatically advanced natural language processing (NLP) tasks, including clinical NLP tasks such as concept extraction.
BioBERT: pre-trained biomedical language representation model for biomedical text mining
Jinhyuk Lee, Wonjin Yoon, Sungdong Kim, Donghyeon Kim, Sunkyu Kim, Chan Ho So, and Jaewoo Kang. 2019 · 1901
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Publicly available clinical bert embeddings
Emily Alsentzer, John R Murphy, Willie Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019 · 1904
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Clinicalbert: Modeling clinical notes and predicting hospital readmission
Kexin Huang, Jaan Altosaar, and Rajesh Ranganath. 2019 · 1904
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Clinical named entity recognition: Challenges and opportunities
Srinivasa Rao Kundeti, J Vijayananda, Srikanth Mujjiga, and M Kalyan. 2016 · 1945
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The specialist lexicon
Allen C Browne, Alexa T McCray, and Suresh Srinivasan. 2000 · 2000
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Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010
Berry De Bruijn, Colin Cherry, Svetlana Kiritchenko, Joel Martin, and Xiaodan Zhu. 2011 · 2010
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio. 2010 · 2010
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Semantic similarity and relatedness between clinical terms: an experimental study
Serguei Pakhomov, Bridget McInnes, Terrence Adam, Ying Liu, Ted Pedersen, and Genevieve B Melton. 2010 · 2010
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2010 i2b2/va challenge on concepts, assertions, and relations in clinical text
Özlem Uzuner, Brett R South, Shuying Shen, and Scott L DuVall. 2011 · 2010
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Stanford’s multi-pass sieve coreference resolution system at the conll-2011 shared task
Heeyoung Lee, Yves Peirsman, Angel Chang, Nathanael Chambers, Mihai Surdeanu, and Dan Jurafsky. 2011 · 2011
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Automatic extraction of relations between medical concepts in clinical texts
Bryan Rink, Sanda Harabagiu, and Kirk Roberts. 2011 · 2011
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Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases
Hua Xu, Zhenming Fu, Anushi Shah, Yukun Chen, Neeraja B Peterson, Qingxia Chen, Subramani Mani, Mia A Levy, Qi Dai, and Josh C Denny. 2011 · 2011
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Japanese and korean voice search
Mike Schuster and Kaisuke Nakajima. 2012 · 2012
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Evaluating temporal relations in clinical text: 2012 i2b2 challenge
Weiyi Sun, Anna Rumshisky, and Ozlem Uzuner. 2013 · 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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Overview of the share/clef ehealth evaluation lab 2013
Hanna Suominen, Sanna Salanterä, Sumithra Velupillai, Wendy W Chapman, Guergana Savova, Noemie Elhadad, Sameer Pradhan, Brett R South, Danielle L Mowery, Gareth JF Jones, et al. 2013 · 2013
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Recognizing clinical entities in hospital discharge summaries using structural support vector machines with word representation features
Buzhou Tang, Hongxin Cao, Yonghui Wu, Min Jiang, and Hua Xu. 2013 · 2013
Cited alongside, same era.
Overview of the share/clef ehealth evaluation lab 2014
Liadh Kelly, Lorraine Goeuriot, Hanna Suominen, Tobias Schreck, Gondy Leroy, Danielle L Mowery, Sumithra Velupillai, Wendy W Chapman, David Martinez, Guido Zuccon, et al. 2014 · 2014
Cited alongside, same era.
Glove: Global vectors for word representation
Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014 · 2014
Cited alongside, same era.
Semeval-2014 task 7: Analysis of clinical text
Sameer Pradhan, Noémie Elhadad, Wendy Chapman, Suresh Manandhar, and Guergana Savova. 2014 · 2014
Cited alongside, same era.
Automated systems for the de-identification of longitudinal clinical narratives: Overview of 2014 i2b2/uthealth shared task track 1
Amber Stubbs, Christopher Kotfila, and Özlem Uzuner. 2015 · 2014
Cited alongside, same era.
Corpus domain effects on distributional semantic modeling of medical terms
Serguei VS Pakhomov, Greg Finley, Reed McEwan, Yan Wang, and Genevieve B Melton. 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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Bidirectional attention flow for machine comprehension
Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, and Hannaneh Hajishirzi. 2016 · 2016
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Deep learning with word embeddings improves biomedical named entity recognition
Maryam Habibi, Leon Weber, Mariana Neves, David Luis Wiegandt, and Ulf Leser. 2017 · 2017
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Entity recognition from clinical texts via recurrent neural network
Zengjian Liu, Ming Yang, Xiaolong Wang, Qingcai Chen, Buzhou Tang, Zhe Wang, and Hua Xu. 2017 · 2017
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UTH_CCB: a report for semeval 2014–task 7 analysis of clinical text
Yaoyun Zhang, Jingqi Wang, Buzhou Tang, Yonghui Wu, Min Jiang, Yukun Chen, and Hua Xu. 2014 · 2014
Cited alongside, same era.
Application of word embeddings in biomedical named entity recognition tasks
FX Chang, J Guo, WR Xu, and S Relly Chung. 2015 · 2015
Cited alongside, same era.
Semeval-2015 task 14: Analysis of clinical text
Noémie Elhadad, Sameer Pradhan, Sharon Gorman, Suresh Manandhar, Wendy Chapman, and Guergana Savova. 2015 · 2015
Cited alongside, same era.
Recognizing disjoint clinical concepts in clinical text using machine learning-based methods
Buzhou Tang, Qingcai Chen, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang, and Hua Xu. 2015 · 2015
Cited alongside, same era.
A study of neural word embeddings for named entity recognition in clinical text
Yonghui Wu, Jun Xu, Min Jiang, Yaoyun Zhang, and Hua Xu. 2015 · 2015
Cited alongside, same era.
Tensorflow: a system for large-scale machine learning
Martín Abadi, Paul Barham, Jianmin Chen, Zhifeng Chen, Andy Davis, Jeffrey Dean, Matthieu Devin, Sanjay Ghemawat, Geoffrey Irving, Michael Isard, et al. 2016 · 2016
Cited alongside, same era.
Semeval-2016 task 12: Clinical tempeval
Steven Bethard, Guergana Savova, Wei-Te Chen, Leon Derczynski, James Pustejovsky, and Marc Verhagen. 2016 · 2016
Cited alongside, same era.
Word embedding based correlation model for question/answer matching
Yikang Shen, Wenge Rong, Nan Jiang, Baolin Peng, Jie Tang, and Zhang Xiong. 2017 · 2017
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Recurrent neural networks with specialized word embeddings for health-domain named-entity recognition
Iñigo Jauregi Unanue, Ehsan Zare Borzeshi, and Massimo Piccardi. 2017 · 2017
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Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017 · 2017
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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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Named entity recognition using neural networks for clinical notes
Edson Florez, Frédéric Precioso, Michel Riveill, and Romaric Pighetti. 2018 · 2018
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Automatic information extraction from unstructured mammography reports using distributed semantics
Anupama Gupta, Imon Banerjee, and Daniel L Rubin. 2018 · 2018
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Deep contextualized word representations
Matthew E Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018 · 2018
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A Frame-Based NLP System for Cancer-Related Information Extraction
Yuqi Si and Kirk Roberts. 2018 · 2018
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Using clinical natural language processing for health outcomes research: Overview and actionable suggestions for future advances
Sumithra Velupillai, Hanna Suominen, Maria Liakata, Angus Roberts, Anoop D Shah, Katherine Morley, David Osborn, Joseph Hayes, Robert Stewart, Johnny Downs, et al. 2018 · 2018
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Clinical concept extraction with contextual word embedding
Henghui Zhu, Ioannis Ch Paschalidis, and Amir Tahmasebi. 2018 · 2018
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