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Machine learning models depend on the quality of input data.
On the convergence of adam and beyond
Sashank J Reddi, Satyen Kale, and Sanjiv Kumar. 2019 · 1904
Earlier work this paper cites.
Nltk: The natural language toolkit
Edward Loper and Steven Bird. 2002 · 2002
Earlier work this paper cites.
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Denis Jered McInerney, Borna Dabiri, Anne-Sophie Touret, Geoffrey Young, Jan-Willem van de Meent, and Byron C Wallace. 2020 · 2004
Earlier work this paper cites.
The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich. 2006 · 2006
Earlier work this paper cites.
Critical issues in an electronic documentation system
Charlene R Weir and Jonathan R Nebeker. 2007 · 2007
Earlier work this paper cites.
Off the record–avoiding the pitfalls of going electronic
Pamela Hartzband, Jerome Groopman, et al. 2008 · 2008
Earlier work this paper cites.
Biomedical informatics in the education of physicians
Edward H Shortliffe. 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.
Biomedical informatics: changing what physicians need to know and how they learn
William W Stead, John R Searle, Henry E Fessler, Jack W Smith, and Edward H Shortliffe. 2011 · 2011
Earlier work this paper cites.
Redundancy in electronic health record corpora: analysis, impact on text mining performance and mitigation strategies
Raphael Cohen, Michael Elhadad, and Noémie Elhadad. 2013 · 2013
Earlier work this paper cites.
What do physicians read (and ignore) in electronic progress notes?
PJ Brown, JL Marquard, B Amster, M Romoser, J Friderici, S Goff, and D Fisher. 2014 · 2014
Earlier work this paper cites.
Unfolding physiological state: Mortality modelling in intensive care units
Marzyeh Ghassemi, Tristan Naumann, Finale Doshi-Velez, Nicole Brimmer, Rohit Joshi, Anna Rumshisky, and Peter Szolovits. 2014 · 2014
Earlier work this paper cites.
Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system
Harlan M Krumholz. 2014 · 2014
Earlier work this paper cites.
Mth-med-spel-chek of mt-herald
Rajasekharan Narayanaswamy. 2014 · 2014
Cited alongside, same era.
Openmedspel of e-medtools (version 2.0.0)
R. Robinson. 2014 · 2014
Cited alongside, same era.
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Chaitanya Shivade, Preethi Raghavan, Eric Fosler-Lussier, Peter J Embi, Noemie Elhadad, Stephen B Johnson, and Albert M Lai. 2014 · 2014
Cited alongside, same era.
Entity linking for biomedical literature
Jin Guang Zheng, Daniel Howsmon, Boliang Zhang, Juergen Hahn, Deborah McGuinness, James Hendler, and Heng Ji. 2014 · 2014
Cited alongside, same era.
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Mohit Iyyer, Varun Manjunatha, Jordan Boyd-Graber, and Hal Daumé III. 2015 · 2015
Cited alongside, same era.
Extracting information from the text of electronic medical records to improve case detection: a systematic review
Why doctors hate their computers
Atul Gawande. 2018 · 2018
Later among the works it cites.
Deep ehr: Chronic disease prediction using medical notes
Jingshu Liu, Zachariah Zhang, and Narges Razavian. 2018 · 2018
Later among the works it cites.
Factors related to physician burnout and its consequences: a review
Rikinkumar S Patel, Ramya Bachu, Archana Adikey, Meryem Malik, and Mansi Shah. 2018 · 2018
Later among the works it cites.
Benchmarking deep learning models on large healthcare datasets
Sanjay Purushotham, Chuizheng Meng, Zhengping Che, and Yan Liu. 2018 · 2018
Later among the works it cites.
Scalable and accurate deep learning with electronic health records
Alvin Rajkomar, Eyal Oren, Kai Chen, Andrew M Dai, Nissan Hajaj, Michaela Hardt, Peter J Liu, Xiaobing Liu, Jake Marcus, Mimi Sun, et al. 2018 · 2018
Later among the works it cites.
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Elizabeth Ford, John A Carroll, Helen E Smith, Donia Scott, and Jackie A Cassell. 2016 · 2016
Cited alongside, same era.
Opinion: When the doctor must choose between her patients and her notes
M Zeng. 2016 · 2016
Cited alongside, same era.
A novel data-driven workflow combining literature and electronic health records to estimate comorbidities burden for a specific disease: a case study on autoimmune comorbidities in patients with celiac disease
Jean-Baptiste Escudié, Bastien Rance, Georgia Malamut, Sherine Khater, Anita Burgun, Christophe Cellier, and Anne-Sophie Jannot. 2017 · 2017
Cited alongside, same era.
Multitask learning and benchmarking with clinical time series data
Hrayr Harutyunyan, Hrant Khachatrian, David C Kale, and Aram Galstyan. 2017 · 2017
Cited alongside, same era.
Reproducibility in critical care: a mortality prediction case study
Alistair EW Johnson, Tom J Pollard, and Roger G Mark. 2017 · 2017
Cited alongside, same era.
Extractive summarization of ehr discharge notes
Emily Alsentzer and Anne Kim. 2018 · 2018
Cited alongside, same era.
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Willie Boag, Dustin Doss, Tristan Naumann, and Peter Szolovits. 2018 · 2018
Cited alongside, same era.
Yanshan Wang, Liwei Wang, Majid Rastegar-Mojarad, Sungrim Moon, Feichen Shen, Naveed Afzal, Sijia Liu, Yuqun Zeng, Saeed Mehrabi, Sunghwan Sohn, et al. 2018 · 2018
Later among the works it cites.
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Emily Alsentzer, John Murphy, William Boag, Wei-Hung Weng, Di Jin, Tristan Naumann, and Matthew McDermott. 2019 · 2019
Later among the works it cites.
A novel system for extractive clinical note summarization using EHR data
Jennifer Liang, Ching-Huei Tsou, and Ananya Poddar. 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, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala. 2019 · 2019
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Universal adversarial triggers for nlp
Eric Wallace, Shi Feng, Nikhil Kandpal, Matt Gardner, and Sameer Singh. 2019 · 2019
Later among the works it cites.
Clinical concept extraction for document-level coding
Sarah Wiegreffe, Edward Choi, Sherry Yan, Jimeng Sun, and Jacob Eisenstein. 2019 · 2019
Later among the works it cites.
Generating soap notes from doctor-patient conversations
Kundan Krishna, Sopan Khosla, Jeffrey P. Bigham, and Zachary C. Lipton. 2020 · 2020
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Report of the amia ehr-2020 task force on the status and future direction of ehrs
Thomas H Payne, Sarah Corley, Theresa A Cullen, Tejal K Gandhi, Linda Harrington, Gilad J Kuperman, John E Mattison, David P McCallie, Clement J McDonald, Paul C Tang, et al. 2015 · 2020
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