References¶
- BPW20
Patrik Bachtiger, Nicholas S Peters, and Simon LF Walsh. Machine learning for COVID-19—asking the right questions. The Lancet Digital Health, 2(8):e391–e392, August 2020. doi:10.1016/S2589-7500(20)30162-X.
- BFvdL+20
Michael Blastland, Alexandra L. J. Freeman, Sander van der Linden, Theresa M. Marteau, and David Spiegelhalter. Five rules for evidence communication. Nature, 587(7834):362–364, November 2020. doi:10.1038/d41586-020-03189-1.
- CRAM15
Gary S. Collins, Johannes B. Reitsma, Douglas G. Altman, and Karel G.M. Moons. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): The TRIPOD Statement. Annals of Internal Medicine, 162(1):55, January 2015. doi:10.7326/M14-0697.
- GSH20
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- KBahnikFurnkranz20
Tomáš Kliegr, Štěpán Bahník, and Johannes Fürnkranz. A review of possible effects of cognitive biases on interpretation of rule-based machine learning models. arXiv:1804.02969 [cs, stat], June 2020. arXiv:1804.02969.
- MVF+20
Cristina Menni, Ana M. Valdes, Maxim B. Freidin, Carole H. Sudre, Long H. Nguyen, David A. Drew, Sajaysurya Ganesh, Thomas Varsavsky, M. Jorge Cardoso, Julia S. El-Sayed Moustafa, Alessia Visconti, Pirro Hysi, Ruth C. E. Bowyer, Massimo Mangino, Mario Falchi, Jonathan Wolf, Sebastien Ourselin, Andrew T. Chan, Claire J. Steves, and Tim D. Spector. Real-time tracking of self-reported symptoms to predict potential COVID-19. Nature Medicine, 26(7):1037–1040, July 2020. doi:10.1038/s41591-020-0916-2.
- Ody20
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- RHH+18
Alvin Rajkomar, Michaela Hardt, Michael D. Howell, Greg Corrado, and Marshall H. Chin. Ensuring Fairness in Machine Learning to Advance Health Equity. Annals of Internal Medicine, 169(12):866, December 2018. doi:10.7326/M18-1990.
- Ros94
Robert Rosenthal. Science and Ethics in Conducting, Analyzing, and Reporting Psychological Research. Psychological Science, 5(3):127–134, May 1994. doi:10.1111/j.1467-9280.1994.tb00646.x.
- SBB+20
Andrea Saltelli, Gabriele Bammer, Isabelle Bruno, Erica Charters, Monica Di Fiore, Emmanuel Didier, Wendy Nelson Espeland, John Kay, Samuele Lo Piano, Deborah Mayo, Roger Pielke Jr, Tommaso Portaluri, Theodore M. Porter, Arnald Puy, Ismael Rafols, Jerome R. Ravetz, Erik Reinert, Daniel Sarewitz, Philip B. Stark, Andrew Stirling, Jeroen van der Sluijs, and Paolo Vineis. Five ways to ensure that models serve society: a manifesto. Nature, 582(7813):482–484, June 2020. doi:10.1038/d41586-020-01812-9.
- Spi20
David Spiegelhalter. Use of “normal” risk to improve understanding of dangers of covid-19. BMJ, pages m3259, September 2020. doi:10.1136/bmj.m3259.
- SG20
Harini Suresh and John V. Guttag. A Framework for Understanding Unintended Consequences of Machine Learning. arXiv:1901.10002 [cs, stat], February 2020. arXiv:1901.10002.
- WWB+20
Elizabeth J. Williamson, Alex J. Walker, Krishnan Bhaskaran, Seb Bacon, Chris Bates, Caroline E. Morton, Helen J. Curtis, Amir Mehrkar, David Evans, Peter Inglesby, Jonathan Cockburn, Helen I. McDonald, Brian MacKenna, Laurie Tomlinson, Ian J. Douglas, Christopher T. Rentsch, Rohini Mathur, Angel Y. S. Wong, Richard Grieve, David Harrison, Harriet Forbes, Anna Schultze, Richard Croker, John Parry, Frank Hester, Sam Harper, Rafael Perera, Stephen J. W. Evans, Liam Smeeth, and Ben Goldacre. Factors associated with COVID-19-related death using OpenSAFELY. Nature, 584(7821):430–436, August 2020. doi:10.1038/s41586-020-2521-4.