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As machine learning (ML) models gain traction in clinical applications, understanding the impact of clinician and societal biases on ML models is increasingly important.
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James J Heckman · 1976
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Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods
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The effect of race and sex on physicians’ recommendations for cardiac catheterization
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Jing Jiang · 2008
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Examining racial disparities in colorectal cancer care
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A kernel two-sample test
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Daniel T Lackland · 2014
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Katharine E Henry, David N Hager, Peter J Pronovost, and Suchi Saria · 2015
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Assessment of clinical criteria for sepsis: for the third international consensus definitions for sepsis and septic shock (sepsis-3)
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Nicolas Courty, Rémi Flamary, Amaury Habrard, and Alain Rakotomamonjy · 2017
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Stacie L Daugherty, Irene V Blair, Edward P Havranek, Anna Furniss, L Miriam Dickinson, Elhum Karimkhani, Deborah S Main, and Frederick A Masoudi · 2017
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The problem of infra-marginality in outcome tests for discrimination
Camelia Simoiu, Sam Corbett-Davies, and Sharad Goel · 2017
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Gopal K Singh, Gem P Daus, Michelle Allender, Christine T Ramey, Elijah K Martin, Chrisp Perry, Andrew A De Los Reyes, and Ivy P Vedamuthu · 2017
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Gender shades: Intersectional accuracy disparities in commercial gender classification
Joy Buolamwini and Timnit Gebru · 2018
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Early prediction of mrsa infections using electronic health records
Thomas Hartvigsen, Cansu Sen, Sarah Brownell, Erin Teeple, Xiangnan Kong, and Elke A Rundensteiner · 2018
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Sex differences in the presentation and perception of symptoms among young patients with myocardial infarction: evidence from the virgo study (variation in recovery: role of gender on outcomes of young ami patients)
Judith H Lichtman, Erica C Leifheit, Basmah Safdar, Haikun Bao, Harlan M Krumholz, Nancy P Lorenze, Mitra Daneshvar, John A Spertus, and Gail D’Onofrio · 2018
Deep learning applied to chest x-rays: Exploiting and preventing shortcuts
Sarah Jabbour, David Fouhey, Ella Kazerooni, Michael W Sjoding, and Jenna Wiens · 2020
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Identifying and correcting label bias in machine learning
Heinrich Jiang and Ofir Nachum · 2020
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Mimic-iv
Alistair Johnson, Lucas Bulgarelli, Tom Pollard, Steven Horng, Leo Anthony Celi, and Roger Mark · 2020
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Intersectionality and health inequities for gender minority blacks in the us
Elle Lett, Nadia L Dowshen, and Kellan E Baker · 2020
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Hospitalization and mortality among black patients and white patients with Covid-19
Eboni G Price-Haywood, Jeffrey Burton, Daniel Fort, and Leonardo Seoane · 2020
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Sepsis trends: increasing incidence and decreasing mortality, or changing denominator?
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Learning from binary labels with instance-dependent noise
Aditya Krishna Menon, Brendan Van Rooyen, and Nagarajan Natarajan · 2018
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A generalizable, data-driven approach to predict daily risk of clostridium difficile infection at two large academic health centers
Jeeheh Oh, Maggie Makar, Christopher Fusco, Robert McCaffrey, Krishna Rao, Erin E Ryan, Laraine Washer, Lauren R West, Vincent B Young, John Guttag, et al · 2018
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Fast threshold tests for detecting discrimination
Emma Pierson, Sam Corbett-Davies, and Sharad Goel · 2018
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Measuring the discrepancy between conditional distributions: Methods, properties and applications
Shujian Yu, Ammar Shaker, Francesco Alesiani, and Jose C Principe · 2018
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A multicenter, scan-rescan, human and machine learning cmr study to test generalizability and precision in imaging biomarker analysis
Anish N Bhuva, Wenjia Bai, Clement Lau, Rhodri H Davies, Yang Ye, Heeraj Bulluck, Elisa McAlindon, Veronica Culotta, Peter P Swoboda, Gabriella Captur, et al · 2019
Cited alongside, same era.
Comparison of automated sepsis identification methods and electronic health record–based sepsis phenotyping: improving case identification accuracy by accounting for confounding comorbid conditions
Katharine E Henry, David N Hager, Tiffany M Osborn, Albert W Wu, and Suchi Saria · 2019
Cited alongside, same era.
The fairness of risk scores beyond classification: Bipartite ranking and the xauc metric
Nathan Kallus and Angela Zhou · 2019
Cited alongside, same era.
Chanu Rhee and Michael Klompas · 2020
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Clinical performance evaluation of a machine learning system for predicting hospital-acquired clostridium difficile infection
Erin Teeple, Thomas Hartvigsen, Cansu Sen, Kajal T Claypool, and Elke A Rundensteiner · 2020
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Hidden in plain sight—reconsidering the use of race correction in clinical algorithms, 2020
Darshali A Vyas, Leo G Eisenstein, and David S Jones · 2020
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Confidence scores make instance-dependent label-noise learning possible
Antonin Berthon, Bo Han, Gang Niu, Tongliang Liu, and Masashi Sugiyama · 2021
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Instance-dependent positive and unlabeled learning with labeling bias estimation
Chen Gong, Qizhou Wang, Tongliang Liu, Bo Han, Jane J You, Jian Yang, and Dacheng Tao · 2021
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Recalibrating the use of race in medical research
John PA Ioannidis, Neil R Powe, and Clyde Yancy · 2021
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Disparities in covid-19 outcomes by race, ethnicity, and socioeconomic status: a systematic-review and meta-analysis
Shruti Magesh, Daniel John, Wei Tse Li, Yuxiang Li, Aidan Mattingly-App, Sharad Jain, Eric Y Chang, and Weg M Ongkeko · 2021
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Structural racism and immigrant health in the united states
Supriya Misra, Simona C Kwon, Ana F Abraído-Lanza, Perla Chebli, Chau Trinh-Shevrin, and Stella S Yi · 2021
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Learning decision thresholds for risk stratification models from aggregate clinician behavior
Birju S Patel, Ethan Steinberg, Stephen R Pfohl, and Nigam H Shah · 2021
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An algorithmic approach to reducing unexplained pain disparities in underserved populations
Emma Pierson, David M Cutler, Jure Leskovec, Sendhil Mullainathan, and Ziad Obermeyer · 2021
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Respecting autonomy and enabling diversity: The effect of eligibility and enrollment on research data demographics
Kayte Spector-Bagdady, Shengpu Tang, Sarah Jabbour, W Nicholson Price, Ana Bracic, Melissa S Creary, Sachin Kheterpal, Chad M Brummett, and Jenna Wiens · 2021
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Fair classification with group-dependent label noise
Jialu Wang, Yang Liu, and Caleb Levy · 2021
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Prospective, multi-site study of patient outcomes after implementation of the trews machine learning-based early warning system for sepsis
Roy Adams, Katharine E Henry, Anirudh Sridharan, Hossein Soleimani, Andong Zhan, Nishi Rawat, Lauren Johnson, David N Hager, Sara E Cosgrove, Andrew Markowski, et al · 2022
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