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How do we most effectively treat a disease or condition? Ideally, we could consult a database of evidence gleaned from clinical trials to answer such questions.
Scibert: Pretrained contextualized embeddings for scientific text
Iz Beltagy, Arman Cohan, and Kyle Lo. 2019 · 1903
Earlier work this paper cites.
Seventy-five trials and eleven systematic reviews a day: how will we ever keep up?
Hilda Bastian, Paul Glasziou, and Iain Chalmers. 2010 · 2010
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, J. Vanderplas, A. Passos, D. Cournapeau, M. Brucher, M. Perrot, and E. Duchesnay. 2011 · 2011
Earlier work this paper cites.
Learning phrase representations using RNN encoder–decoder for statistical machine translation
Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014 · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2014 · 2014
Earlier work this paper cites.
Neural machine translation by jointly learning to align and translate
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. 2015 · 2015
Earlier work this paper cites.
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
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018 · 2018
Cited alongside, same era.
The impact of patient, intervention, comparison, outcome (PICO) as a search strategy tool on literature search quality: a systematic review
Mette Brandt Eriksen and Tove Faber Frandsen. 2018 · 2018
Cited alongside, same era.
A corpus with multi-level annotations of patients, interventions and outcomes to support language processing for medical literature
Benjamin Nye, Junyi Jessy Li, Roma Patel, Yinfei Yang, Iain Marshall, Ani Nenkova, and Byron Wallace. 2018 · 2018
Cited alongside, same era.
Inferring which medical treatments work from reports of clinical trials
Eric Lehman, Jay DeYoung, Regina Barzilay, and Byron C. Wallace. 2019 · 2019
Toward systematic review automation: A practical guide to using machine learning tools in research synthesis
Iain J. Marshall and Byron C. Wallace. 2019 · 2019
Later among the works it cites.
Scispacy: Fast and robust models for biomedical natural language processing
Mark Neumann, Daniel King, Iz Beltagy, and Waleed Ammar. 2019 · 2019
Later among the works it cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Later among the works it cites.
Don’t stop pretraining: Adapt language models to domains and tasks
Suchin Gururangan, Ana Marasović, Swabha Swayamdipta, Kyle Lo, Iz Beltagy, Doug Downey, and Noah A. Smith. 2020 · 2020
Closest in time.
Fact or fiction: Verifying scientific claim
David Wadden, Kyle Lo, Lucy Lu Wang, Shanchuan Lin, Madeleine van Zuylen, Arman Cohan, and Hannaneh Hajishirzi. 2020 · 2020
Closest in time.
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Cited alongside, same era.
Roberta: A robustly optimized bert pretraining approach
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019 · 2019
Cited alongside, same era.