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One of the biggest challenges that prohibit the use of many current NLP methods in clinical settings is the availability of public datasets.
A study of abbreviations in clinical notes
Hua Xu, Peter D. Stetson, and Carol Friedman. 2007 · 2007
Earlier work this paper cites.
Understanding PubMed(R) user search behavior through log analysis
R. Islamaj Dogan, G. C. Murray, A. Neveol, and Z. Lu. 2009 · 2009
Earlier work this paper cites.
Neural Machine Translation by Jointly Learning to Align and Translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
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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
Earlier work this paper cites.
Doctor AI: Predicting Clinical Events via Recurrent Neural Networks
Edward Choi, Mohammad Taha Bahadori, Andy Schuetz, Walter F. Stewart, and Jimeng Sun. 2015 · 2015
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Exploiting Task-Oriented Resources to Learn Word Embeddings for Clinical Abbreviation Expansion
Yue Liu, Tao Ge, Kusum S. Mathews, Heng Ji, and Deborah L. McGuinness. 2018 · 2015
Earlier work this paper cites.
Towards Comprehensive Clinical Abbreviation Disambiguation Using Machine-Labeled Training Data
Gregory P. Finley, Serguei V.S. Pakhomov, Reed McEwan, and Genevieve B. Melton. 2016 · 2016
Cited alongside, same era.
Neural Document Embeddings for Intensive Care Patient Mortality Prediction
Paulina Grnarova, Florian Schmidt, Stephanie L. Hyland, and Carsten Eickhoff. 2016 · 2016
Cited alongside, same era.
MIMIC-III, a freely accessible critical care database
Alistair E.W. 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
Cited alongside, same era.
Enriching Word Vectors with Subword Information
Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017 · 2017
Cited alongside, same era.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017 · 2017
Clinical information extraction applications: A literature review
Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, and Hongfang Liu. 2018 · 2018
Later among the works it cites.
Deep Contextualized Biomedical Abbreviation Expansion
Qiao Jin, Jinling Liu, and Xinghua Lu. 2019 · 2019
Later among the works it cites.
A Neural Topic-Attention Model for Medical Term Abbreviation Disambiguation
Irene Li, Michihiro Yasunaga, Muhammed Yavuz Nuzumlalı, Cesar Caraballo, Shiwani Mahajan, Harlan Krumholz, and Dragomir Radev. 2019 · 2019
Later among the works it cites.
Marta Skreta, Aryan Arbabi, Jixuan Wang, and Michael Brudno. 2019 · 2019
Later among the works it cites.
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Cited alongside, same era.
ADAM: another database of abbreviations in MEDLINE
W. Zhou, V. I. Torvik, and N. R. Smalheiser. 2006 · 2017
Cited alongside, same era.
A convolutional route to abbreviation disambiguation in clinical text
Venkata Joopudi, Bharath Dandala, and Murthy Devarakonda. 2018 · 2018
Cited alongside, same era.
Kevin Clark, Minh-Thang Luong, Quoc V. Le, and Christopher D. Manning. 2020 · 2020
Closest in time.
Inferring multimodal latent topics from electronic health records
Yue Li, Pratheeksha Nair, Xing Han Lu, Zhi Wen, Yuening Wang, Amir Ardalan Kalantari Dehaghi, Yan Miao, Weiqi Liu, Tamas Ordog, Joanna M. Biernacka, Euijung Ryu, Janet E. Olson, Mark A. Frye, Aihua Liu, Liming Guo, Ariane Marelli, Yuri Ahuja, Jose Davila-Velderrain, and Manolis Kellis. 2020 · 2020
Closest in time.