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A growing body of work uses the paradigm of algorithmic fairness to frame the development of techniques to anticipate and proactively mitigate the introduction or exacerbation of health inequities that may follow from the use of model-guided decision-making.
Fairness in recommendation ranking through pairwise comparisons
Alex Beutel, Jilin Chen, Tulsee Doshi, Hai Qian, Li Wei, Yi Wu, Lukasz Heldt, Zhe Zhao, Lichan Hong, Ed H. Chi, and Cristos Goodrow · 1903
Earlier work this paper cites.
Learning Fair Representations
Richard S Zemel, Yu Wu, Kevin Swersky, Toniann Pitassi, and Cynthia Dwork · 1938
Earlier work this paper cites.
Learning Adversarially Fair and Transferable Representations
David Madras, Elliot Creager, Toniann Pitassi, and Richard Zemel · 1938
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Ensuring Fairness in Machine Learning to Advance Health Equity
Alvin Rajkomar, Michaela Hardt, Michael D. Howell, Greg Corrado, and Marshall H. Chin · 1990
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Recovery of Information and Adjustment for Dependent Censoring Using Surrogate Markers
James M. Robins and Andrea Rotnitzky · 1992
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Correcting for noncompliance and dependent censoring in an aids clinical trial with inverse probability of censoring weighted (ipcw) log-rank tests
James M Robins and Dianne M Finkelstein · 2000
Earlier work this paper cites.
Unified methods for censored longitudinal data and causality
Mark J Van der Laan, MJ Laan, and James M Robins · 2003
Earlier work this paper cites.
Tree-based multivariate regression and density estimation with right-censored data
Annette M. Molinaro, Sandrine Dudoit, and Mark J. Van Der Laan · 2004
Earlier work this paper cites.
Efficacy and safety of cholesterol-lowering treatment: prospective meta-analysis of data from 90 056 participants in 14 randomised trials of statins
Cholesterol Treatment Trialists et al · 2005
Earlier work this paper cites.
Decision curve analysis: a novel method for evaluating prediction models
Andrew J Vickers and Elena B Elkin · 2006
Earlier work this paper cites.
Method for evaluating prediction models that apply the results of randomized trials to individual patients
Andrew J Vickers, Michael W Kattan, and Daniel J Sargent · 2007
Earlier work this paper cites.
Evaluating prediction rules for t-year survivors with censored regression models
Hajime Uno, Tianxi Cai, Lu Tian, and Lee-Jen J. Wei · 2007
Earlier work this paper cites.
Managing dyslipidemia in chronic kidney disease
Charles R Harper and Terry A Jacobson · 2008
Earlier work this paper cites.
Building classifiers with independency constraints
Toon Calders, Faisal Kamiran, and Mykola Pechenizkiy · 2009
Earlier work this paper cites.
A conceptual framework for action on the social determinants of health
World Health Organization et al · 2010
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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 · 2011
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A kernel two-sample test
Arthur Gretton, Karsten M Borgwardt, Malte J Rasch, Bernhard Schölkopf, and Alexander Smola · 2012
Earlier work this paper cites.
Medical Decision Making
Harold C. Sox, Michael C. Higgins, and Douglas K. Owens · 2013
Earlier work this paper cites.
Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
Earlier work this paper cites.
Management of cardiovascular risk in patients with rheumatoid arthritis: evidence and expert opinion
Inge A.M. van den Oever, Alper M. van Sijl, and Michael T. Nurmohamed · 2013
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Review and comparison of roc curve estimators for a time-dependent outcome with marker-dependent censoring
Paul Blanche, Jean-François Dartigues, and Hélène Jacqmin-Gadda · 2013
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2013 acc/aha guideline on the assessment of cardiovascular risk: a report of the american college of cardiology/american heart association task force on practice guidelines
David C Goff, Donald M Lloyd-Jones, Glen Bennett, Sean Coady, Ralph B D’agostino, Raymond Gibbons, Philip Greenland, Daniel T Lackland, Daniel Levy, Christopher J O’donnell, et al · 2014
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2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: A report of the american college of cardiology/american heart association task force on practice guidelines
Neil J. Stone, Jennifer G. Robinson, Alice H. Lichtenstein, C. Noel Bairey Merz, Conrad B. Blum, Robert H. Eckel, Anne C. Goldberg, David Gordon, Daniel Levy, Donald M. Lloyd-Jones, Patrick McBride, J. Sanford Schwartz, Susan T. Shero, Sidney C. Smith, Karol Watson, and Peter W.F. F. Wilson · 2014
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Application of new cholesterol guidelines to a population-based sample
Michael J Pencina, Ann Marie Navar-Boggan, Ralph B D’Agostino Sr, Ken Williams, Benjamin Neely, Allan D Sniderman, and Eric D Peterson · 2014
Earlier work this paper cites.
Cardiovascular disease: risk assessment and reduction, including lipid modification (cg181)[online]
National Institute for Health and Care Excellence · 2014
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Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2014
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Equality of Opportunity in Supervised Learning
Moritz Hardt, Eric Price, and Nathan Srebro · 2015
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The Variational Fair Autoencoder
Christos Louizos, Kevin Swersky, Yujia Li, Max Welling, and Richard Zemel · 2015
Earlier work this paper cites.
An Analysis of Calibration and Discrimination Among Multiple Cardiovascular Risk Scores in a Modern Multiethnic Cohort
Andrew P. DeFilippis, Rebekah Young, Christopher J. Carrubba, John W. McEvoy, Matthew J. Budoff, Roger S. Blumenthal, Richard A. Kronmal, Robyn L. McClelland, Khurram Nasir, and Michael J. Blaha · 2015
Earlier work this paper cites.
The acc/aha 2013 pooled cohort equations compared to a korean risk prediction model for atherosclerotic cardiovascular disease
Keum Ji Jung, Yangsoo Jang, Dong Joo Oh, Byung-Hee Oh, Sang Hoon Lee, Seong-Wook Park, Ki-Bae Seung, Hong-Kyu Kim, Young Duk Yun, Sung Hee Choi, et al · 2015
Earlier work this paper cites.
National lipid association recommendations for patient-centered management of dyslipidemia: part 1—full report
Terry A Jacobson, Matthew K Ito, Kevin C Maki, Carl E Orringer, Harold E Bays, Peter H Jones, James M McKenney, Scott M Grundy, Edward A Gill, Robert A Wild, et al · 2015
Earlier work this paper cites.
Causal inference in statistics, social, and biomedical sciences
Guido W Imbens and Donald B Rubin · 2015
Earlier work this paper cites.
Cholesterol, not just cardiovascular risk, is important in deciding who should receive statin treatment
Handrean Soran, Jonathan D Schofield, and Paul N Durrington · 2015
Earlier work this paper cites.
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, Johan Van Der Lei, Nicole Pratt, G Niklas Norén, Yu-Chuan Chuan Li, Paul E Stang, David Madigan, and Patrick B Ryan · 2015
Earlier work this paper cites.
Fair prediction with disparate impact: A study of bias in recidivism prediction instruments
Alexandra Chouldechova · 2016
Earlier work this paper cites.
Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests
Andrew J. Vickers, Ben Van Calster, and Ewout W. Steyerberg · 2016
Earlier work this paper cites.
Calibration of the Pooled Cohort Equations for Atherosclerotic Cardiovascular Disease
Nancy R. Cook and Paul M. Ridker · 2016
Earlier work this paper cites.
Accuracy of the Atherosclerotic Cardiovascular Risk Equation in a Large Contemporary, Multiethnic Population
Jamal S Rana, Grace H Tabada, Matthew D Solomon, Joan C Lo, Marc G Jaffe, Sue Hee Sung, Christie M Ballantyne, and Alan S Go · 2016
Cited alongside, same era.
Diabetes, kidney disease, and cardiovascular outcomes in the jackson heart study
Maryam Afkarian, Ronit Katz, Nisha Bansal, Adolfo Correa, Bryan Kestenbaum, Jonathan Himmelfarb, Ian H De Boer, and Bessie Young · 2016
Cited alongside, same era.
The 2013 acc/aha 10-year atherosclerotic cardiovascular disease risk index is better than score and qrisk ii in rheumatoid arthritis: is it enough?
Gulsen Ozen, Murat Sunbul, Pamir Atagunduz, Haner Direskeneli, Kursat Tigen, and Nevsun Inanc · 2016
Cited alongside, same era.
Interpretation of the evidence for the efficacy and safety of statin therapy
Rory Collins, Christina Reith, Jonathan Emberson, Jane Armitage, Colin Baigent, Lisa Blackwell, Roger Blumenthal, John Danesh, George Davey Smith, David DeMets, et al · 2016
Cited alongside, same era.
Modeling discrete time-to-event data
Gerhard Tutz, Matthias Schmid, et al · 2016
Use of Risk Assessment Tools to Guide Decision-Making in the Primary Prevention of Atherosclerotic Cardiovascular Disease: A Special Report From the American Heart Association and American College of Cardiology
Donald M. Lloyd-Jones, Lynne T. Braun, Chiadi E. Ndumele, Sidney C. Smith Jr, Laurence S. Sperling, Salim S. Virani, and Roger S. Blumenthal · 2019
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Fairness risk measures
Robert C. Williamson and Aditya Krishna Menon · 2019
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Learning controllable fair representations
Jiaming Song, Pratyusha Kalluri, Aditya Grover, Shengjia Zhao, and Stefano Ermon · 2019
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Continuous and discrete-time survival prediction with neural networks
Håvard Kvamme and Ørnulf Borgan · 2019
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Comprehensive comparative effectiveness and safety of first-line antihypertensive drug classes: a systematic, multinational, large-scale analysis
Marc A Suchard, Martijn J Schuemie, Harlan M Krumholz, Seng Chan You, RuiJun Chen, Nicole Pratt, Christian G Reich, Jon Duke, David Madigan, George Hripcsak, et al · 2019
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Cited alongside, same era.
Inherent Trade-Offs in the Fair Determination of Risk Scores
Jon Kleinberg, Sendhil Mullainathan, and Manish Raghavan · 2017
Cited alongside, same era.
Algorithmic Decision Making and the Cost of Fairness
Sam Corbett-Davies, Emma Pierson, Avi Feller, Sharad Goel, and Aziz Huq · 2017
Cited alongside, same era.
The problem of infra-marginality in outcome tests for discrimination
Camelia Simoiu, Sam Corbett-Davies, and Sharad Goel · 2017
Cited alongside, same era.
On fairness and calibration
Geoff Pleiss, Manish Raghavan, Felix Wu, Jon Kleinberg, and Kilian Q. Weinberger · 2017
Cited alongside, same era.
Fairness Constraints: Mechanisms for Fair Classification
Muhammad Bilal Zafar, Isabel Valera, Manuel Gomez Rodriguez, Krishna P Gummadi, Manuel Gomez Rogriguez, and Krishna P Gummadi · 2017
Cited alongside, same era.
Learning Non-Discriminatory Predictors
Blake Woodworth, Suriya Gunasekar, Mesrob I Ohannessian, and Nathan Srebro · 2017
Cited alongside, same era.
Robust optimization for non-convex objectives
Robert Chen, Brendan Lucier, Yaron Singer, and Vasilis Syrgkanis · 2017
Cited alongside, same era.
Later among the works it cites.
A scalable discrete-time survival model for neural networks, jan 2019
Michael F. Gensheimer and Balasubramanian Narasimhan · 2019
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Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
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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
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The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models
Peter C. Austin and Ewout W. Steyerberg · 2019
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A calibration metric for risk scores with survival data
Steve Yadlowsky, Sanjay Basu, and Lu Tian · 2019
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Hidden in Plain Sight — Reconsidering the Use of Race Correction in Clinical Algorithms
Darshali A. Vyas, Leo G. Eisenstein, and David S. Jones · 2020
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Chexclusion: Fairness gaps in deep chest x-ray classifiers
Laleh Seyyed-Kalantari, Guanxiong Liu, Matthew McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2020
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Fair regression for health care spending
Anna Zink and Sherri Rose · 2020
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An empirical characterization of fair machine learning for clinical risk prediction
Stephen R. Pfohl, Agata Foryciarz, and Nigam H. Shah · 2020
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Equity in essence: a call for operationalising fairness in machine learning for healthcare
Judy Wawira Gichoya, Liam G McCoy, Leo Anthony Celi, and Marzyeh Ghassemi · 2020
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Ethical machine learning in healthcare
Irene Y Chen, Emma Pierson, Sherri Rose, Shalmali Joshi, Kadija Ferryman, and Marzyeh Ghassemi · 2020
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Presenting machine learning model information to clinical end users with model facts labels
Mark P Sendak, Michael Gao, Nathan Brajer, and Suresh Balu · 2020
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Pairwise fairness for ranking and regression
Harikrishna Narasimhan, Andrew Cotter, Maya Gupta, and Serena Wang · 2020
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Metric learning for individual fairness
Christina Ilvento · 2020
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Distributionally Robust Neural Networks
Shiori Sagawa, Pang Wei Koh, Tatsunori B Hashimoto, and Percy Liang · 2020
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Minimax pareto fairness: A multi objective perspective
Natalia Martinez, Martin Bertran, and Guillermo Sapiro · 2020
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Machine learning and atherosclerotic cardiovascular disease risk prediction in a multi-ethnic population
Andrew Ward, Ashish Sarraju, Sukyung Chung, Jiang Li, Robert Harrington, Paul Heidenreich, Latha Palaniappan, David Scheinker, and Fatima Rodriguez · 2020
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Network study validating the Pooled Cohort Equation Model, 2020
Jenna Reps and Peter Rijnbeek · 2020
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How Structural Racism Works — Racist Policies as a Root Cause of U.S. Racial Health Inequities
Zinzi D. Bailey, Justin M. Feldman, and Mary T. Bassett · 2020
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Don’t ask if artificial intelligence is good or fair, ask how it shifts power
Pratyusha Kalluri · 2020
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Racial/ethnic disparities in the performance of prediction models for death by suicide after mental health visits
R Yates Coley, Eric Johnson, Gregory E Simon, Maricela Cruz, and Susan M Shortreed · 2021
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Comparison of methods to reduce bias from clinical prediction models of postpartum depression
Yoonyoung Park, Jianying Hu, Moninder Singh, Issa Sylla, Irene Dankwa-Mullan, Eileen Koski, and Amar K Das · 2021
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Addressing bias in prediction models by improving subpopulation calibration
Noam Barda, Gal Yona, Guy N Rothblum, Philip Greenland, Morton Leibowitz, Ran Balicer, Eitan Bachmat, and Noa Dagan · 2021
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Reading race: Ai recognises patient’s racial identity in medical images
Imon Banerjee, Ananth Reddy Bhimireddy, John L Burns, Leo Anthony Celi, Li-Ching Chen, Ramon Correa, Natalie Dullerud, Marzyeh Ghassemi, Shih-Cheng Huang, Po-Chih Kuo, et al · 2021
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Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations
Laleh Seyyed-Kalantari, Haoran Zhang, Matthew McDermott, Irene Y Chen, and Marzyeh Ghassemi · 2021
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Evaluating algorithmic fairness in the presence of clinical guidelines: the case of atherosclerotic cardiovascular disease risk estimation
Agata Foryciarz, Stephen R. Pfohl, Birju Patel, and Nigam H. Shah · 2021
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Measurement and fairness
Abigail Z Jacobs and Hanna Wallach · 2021
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A framework for making predictive models useful in practice
Kenneth Jung, Sehj Kashyap, Anand Avati, Stephanie Harman, Heather Shaw, Ron Li, Margaret Smith, Kenny Shum, Jacob Javitz, Yohan Vetteth, et al · 2021
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Fairness on the ground: Applying algorithmic fairness approaches to production systems
Chloé Bakalar, Renata Barreto, Stevie Bergman, Miranda Bogen, Bobbie Chern, Sam Corbett-Davies, Melissa Hall, Isabel Kloumann, Michelle Lam, Joaquin Quiñonero Candela, et al · 2021
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Minimax group fairness: Algorithms and experiments
Emily Diana, Wesley Gill, Michael Kearns, Krishnaram Kenthapadi, and Aaron Roth · 2021
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Social Determinants in Machine Learning Cardiovascular Disease Prediction Models: A Systematic Review
Yuan Zhao, Erica P. Wood, Nicholas Mirin, Stephanie H. Cook, and Rumi Chunara · 2021
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Evaluating ethical concerns with machine learning to guide advance care planning
Diana Cagliero, Natalie Deuitch, Nigam Shah, and Danton Char · 2021
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