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Machine learning algorithms designed to characterize, monitor, and intervene on human health (ML4H) are expected to perform safely and reliably when operating at scale, potentially outside strict human supervision.
Fair Regression for Health Care Spending
Anna Zink and Sherri Rose · 1901
Earlier work this paper cites.
Relabeling internal and external validity for applied social scientists
Donald T Campbell · 1986
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Physiobank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals
Ary L Goldberger, Luis AN Amaral, Leon Glass, Jeffrey M Hausdorff, Plamen Ch Ivanov, Roger G Mark, Joseph E Mietus, George B Moody, Chung-Kang Peng, and H Eugene Stanley · 2000
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Retraction rates are on the rise
Murat Cokol, Fatih Ozbay, and Raul Rodriguez-Esteban · 2008
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Replicability is not Reproducibility : Nor is it Good Science
Chris Drummond · 2009
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The Registration of Observational Studies—When Metaphors Go Bad
The Editors · 2010
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Should protocols for observational research be registered?
The Lancet · 2010
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Registration of observational studies
Elizabeth Loder, Trish Groves, and Domhnall MacAuley · 2010
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Registration of observational studies: Is it time?
Rebecca J. Williams, Tony Tse, William R. Harlan, and Deborah A. Zarin · 2010
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Commentary: Should Preregistration of Epidemiologic Study Protocols Become Compulsory? Reflections and a Counterproposal
Timothy L. Lash and Jan P. Vandenbroucke · 2012
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Validation of a common data model for active safety surveillance research
J Marc Overhage, Patrick B Ryan, Christian G Reich, Abraham G Hartzema, and Paul E Stang · 2012
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On the reproducibility of science: unique identification of research resources in the biomedical literature
Nicole A. Vasilevsky, Matthew H. Brush, Holly Paddock, Laura Ponting, Shreejoy J. Tripathy, Gregory M. LaRocca, and Melissa A. Haendel · 2013
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The tuh eeg corpus: A big data resource for automated eeg interpretation
A Harati, S Lopez, I Obeid, J Picone, MP Jacobson, and S Tobochnik · 2014
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External validity: From do-calculus to transportability across populations
Judea Pearl, Elias Bareinboim, et al · 2014
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Intelligible models for healthcare: Predicting pneumonia risk and hospital 30-day readmission
Rich Caruana, Yin Lou, Johannes Gehrke, Paul Koch, Marc Sturm, and Noemie Elhadad · 2015
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Observational health data sciences and informatics (ohdsi): opportunities for observational researchers
George Hripcsak, Jon D Duke, Nigam H Shah, Christian G Reich, Vojtech Huser, Martijn J Schuemie, Marc A Suchard, Rae Woong Park, Ian Chi Kei Wong, Peter R Rijnbeek, et al · 2015
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Annotating longitudinal clinical narratives for de-identification: The 2014 i2b2/UTHealth corpus
Amber Stubbs and Özlem Uzuner · 2015
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Uk biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age
Cathie Sudlow, John Gallacher, Naomi Allen, Valerie Beral, Paul Burton, John Danesh, Paul Downey, Paul Elliott, Jane Green, Martin Landray, et al · 2015
Cited alongside, same era.
1,500 scientists lift the lid on reproducibility
Monya Baker · 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
Cited alongside, same era.
Mimic-iii, a freely accessible critical care database
Alistair EW Johnson, Tom J Pollard, Lu Shen, H Lehman Li-wei, Mengling Feng, Mohammad Ghassemi, Benjamin Moody, Peter Szolovits, Leo Anthony Celi, and Roger G Mark · 2016
Cited alongside, same era.
Predicting Clinical Outcomes Across Changing Electronic Health Record Systems
Jen J. Gong, Tristan Naumann, Peter Szolovits, and John V. Guttag · 2017
Cited alongside, same era.
Reproducible Survival Prediction with SEER Cancer Data
Stefan Hegselmann, Leonard Gruelich, Julian Varghese, and Martin Dugas · 2018
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Introducing HL7 FHIR
HL7 · 2018
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Reproducible, Reusable, and Robust Reinforcement Learning, December 2018
Joelle Pineau · 2018
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Generalizability of predictive models for intensive care unit patients
Alistair E. W. Johnson, Tom J. Pollard, and Tristan Naumann · 2018
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Tesla fatal crash: ’autopilot’ mode sped up car before driver killed, report finds
Sam Levin · 2018
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Roundup: 12 healthcare algorithms cleared by the FDA, November 2018
Dave Muoio · 2018
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Peter Henderson, Riashat Islam, Philip Bachman, Joelle Pineau, Doina Precup, and David Meger · 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
Cited alongside, same era.
Are GANs Created Equal? A Large-Scale Study
Mario Lucic, Karol Kurach, Marcin Michalski, Sylvain Gelly, and Olivier Bousquet · 2017
Cited alongside, same era.
On the State of the Art of Evaluation in Neural Language Models
Gábor Melis, Chris Dyer, and Phil Blunsom · 2017
Cited alongside, same era.
Overview of the biobank japan project: study design and profile
Akiko Nagai, Makoto Hirata, Yoichiro Kamatani, Kaori Muto, Koichi Matsuda, Yutaka Kiyohara, Toshiharu Ninomiya, Akiko Tamakoshi, Zentaro Yamagata, Taisei Mushiroda, et al · 2017
Cited alongside, same era.
Why Baseline, April 2017
Verily · 2017
Cited alongside, same era.
The fienberg problem: How to allow human interactive data analysis in the age of differential privacy
Cynthia Dwork and Jonathan Ullman · 2018
Cited alongside, same era.
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Rethinking clinical prediction: Why machine learning must consider year of care and feature aggregation
Bret Nestor, Matthew B. A. McDermott, Geeticka Chauhan, Tristan Naumann, Michael C. Hughes, Anna Goldenberg, and Marzyeh Ghassemi · 2018
Later among the works it cites.
Reproducibility vs. Replicability: A Brief History of a Confused Terminology
Hans E. Plesser · 2018
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The eICU Collaborative Research Database, a freely available multi-center database for critical care research
Tom J. Pollard, Alistair E. W. Johnson, Jesse D. Raffa, Leo A. Celi, Roger G. Mark, and Omar Badawi · 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, Patrik Sundberg, Hector Yee, Kun Zhang, Yi Zhang, Gerardo Flores, Gavin E. Duggan, Jamie Irvine, Quoc Le, Kurt Litsch, Alexander Mossin, Justin Tansuwan, De Wang, James Wexler, Jimbo Wilson, Dana Ludwig, Samuel L. Volchenboum, Katherine Chou, Michael Pearson, Srinivasan Madabushi, Nigam H. Shah, Atul J. Butte, Michael D. Howell, Claire Cui, Greg S. Corrado, and Jeffrey Dean · 2018
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Do ImageNet Classifiers Generalize to ImageNet?
Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar · 2018
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Split learning for health: Distributed deep learning without sharing raw patient data
Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, and Ramesh Raskar · 2018
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Synthea: An approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record
Jason Walonoski, Mark Kramer, Joseph Nichols, Andre Quina, Chris Moesel, Dylan Hall, Carlton Duffett, Kudakwashe Dube, Thomas Gallagher, and Scott McLachlan · 2018
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Google Tries to Patent Healthcare Deep Learning, EHR Analytics, February 2019
Jennifer Bresnick · 2019
Closest in time.
Regulation of predictive analytics in medicine
Ravi B Parikh, Ziad Obermeyer, and Amol S Navathe · 2019
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Improving Patient Care with Machine Learning At Beth Israel Deaconess Medical Center, March 2019
Matt Wood · 2019
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