Critical Appraisal of Fairness Metrics in Clinical Predictive AI | Paper Citation Record

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This paper cites Performance evaluation of predictive AI models to support medical decisions: Overview and guidance.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Performance evaluation of predictive AI models to support medical decisions: Overview and guidance

Reference 1

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This paper cites 93 McDermott MBA, Zhang H, Hansen LH, Angelotti G, Gallifant J.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 93 McDermott MBA, Zhang H, Hansen LH, Angelotti G, Gallifant J

Reference 3

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This paper cites Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data

Reference 4

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This paper cites DOI:10.1093/OED/8693190878.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI DOI:10.1093/OED/8693190878

Reference 6

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This paper cites Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Understanding algorithmic fairness for clinical prediction in terms of subgroup net benefit and health equity

Reference 10

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This paper cites The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI The use of clinical risk factors enhances the performance of BMD in the prediction of hip and osteoporotic fractures in men and women

Reference 11

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This paper cites A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- part 1: binary outcomes

Reference 12

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This paper cites Performance evaluation of predictive AI models to support medical decisions: Overview and guidance.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Performance evaluation of predictive AI models to support medical decisions: Overview and guidance

Reference 13

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This paper cites 54 van der Meijden SL, Wang Y, Arbous MS, Geerts BF, Steyerberg EW, Hernandez-Boussard T.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 54 van der Meijden SL, Wang Y, Arbous MS, Geerts BF, Steyerberg EW, Hernandez-Boussard T

Reference 15

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This paper cites Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Fairness-enhancing mixed effects deep learning improves fairness on in- and out-of-distribution clustered (non-iid) data

Reference 17

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This paper cites Ethical limitations of algorithmic fairness solutions in health care machine learning.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Ethical limitations of algorithmic fairness solutions in health care machine learning

Reference 18

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This paper cites Fairness Through Awareness.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Fairness Through Awareness

Reference 19

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This paper cites Counterfactual Fairness.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Counterfactual Fairness

Reference 20

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This paper cites Certifying and removing disparate impact.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Certifying and removing disparate impact

Reference 21

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This paper cites Measuring and Reducing Racial Bias in a Pediatric Urinary Tract Infection Model.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Measuring and Reducing Racial Bias in a Pediatric Urinary Tract Infection Model

Reference 22

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This paper cites Longitudinal fairness with censorship.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Longitudinal fairness with censorship

Reference 23

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This paper cites 55 Foulds JR, Islam R, Keya KN, Pan S.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 55 Foulds JR, Islam R, Keya KN, Pan S

Reference 24

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This paper cites Detection and mitigation of algorithmic bias via predictive parity.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Detection and mitigation of algorithmic bias via predictive parity

Reference 25

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This paper cites A structured regression approach for evaluating model performance across intersectional subgroups.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI A structured regression approach for evaluating model performance across intersectional subgroups

Reference 26

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This paper cites Fairness in Criminal Justice Risk Assessments: The State of the Art.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Fairness in Criminal Justice Risk Assessments: The State of the Art

Reference 27

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This paper cites 90 Coston A, Ramamurthy KN, Wei D, et al.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 90 Coston A, Ramamurthy KN, Wei D, et al

Reference 28

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This paper cites 92 Wachter S, Mittelstadt B, Russell C.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 92 Wachter S, Mittelstadt B, Russell C

Reference 29

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This paper cites The Measure and Mismeasure of Fairness.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI The Measure and Mismeasure of Fairness

Reference 31

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This paper cites A Closer Look at AUROC and AUPRC under Class Imbalance.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI A Closer Look at AUROC and AUPRC under Class Imbalance

Reference 32

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This paper cites Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Smooth ECE: Principled Reliability Diagrams via Kernel Smoothing

Reference 33

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This paper cites Multi-disciplinary fairness considerations in machine learning for clinical trials.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Multi-disciplinary fairness considerations in machine learning for clinical trials

Reference 36

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This paper cites 107 Nielsen MW, Gissi E, Heidari S, et al.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 107 Nielsen MW, Gissi E, Heidari S, et al

Reference 37

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This paper cites A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI A decomposition of Fisher's information to inform sample size for developing fair and precise clinical prediction models -- Part 2: time-to-event outcomes

Reference 39

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Critical Appraisal of Fairness Metrics in Clinical Predictive AI Coarse race data conceals disparities in clinical risk score performance

Reference 40

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This paper cites Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Evaluating the Impact of Social Determinants on Health Prediction in the Intensive Care Unit

Reference 43

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This paper cites Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Tackling algorithmic bias and promoting transparency in health datasets: the STANDING Together consensus recommendations

Reference 126

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This paper cites Soliciting stakeholders’ fairness notions in child maltreatment predictive systems.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Soliciting stakeholders’ fairness notions in child maltreatment predictive systems

Reference 230

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This paper cites Ethnic classiﬁcations in algorithmic fairness: Concepts, measures and implications in practice.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Ethnic classiﬁcations in algorithmic fairness: Concepts, measures and implications in practice

Reference 243

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This paper cites Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Peeking into a black box, the fairness and generalizability of a MIMIC-III benchmarking model

Reference 488

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Critical Appraisal of Fairness Metrics in Clinical Predictive AI Unresolved cited work

Reference 2010

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Critical Appraisal of Fairness Metrics in Clinical Predictive AI Algorithmic decision making and the cost of fairness

Reference 2016

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This paper cites 41 Corbett-Davies S, Gaebler JD, Nilforoshan H, Shroff R, Goel S.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 41 Corbett-Davies S, Gaebler JD, Nilforoshan H, Shroff R, Goel S

Reference 2018

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Critical Appraisal of Fairness Metrics in Clinical Predictive AI 36 Dwork C, Hardt M, Pitassi T, Reingold O, Zemel R

Reference 2019

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Critical Appraisal of Fairness Metrics in Clinical Predictive AI Fair and interpretable models for survival analysis

Reference 2020

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This paper cites 101 Chien I, Deliu N, Turner RE, Weller A, Villar SS, Kilbertus N.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI 101 Chien I, Deliu N, Turner RE, Weller A, Villar SS, Kilbertus N

Reference 2021

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Critical Appraisal of Fairness Metrics in Clinical Predictive AI 51 Luo Y, Tian Y, Shi M, et al

Reference 2022

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This paper cites Fairness in Machine Learning: A survey.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Fairness in Machine Learning: A survey

Reference 2023

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This paper cites Fairﬂ: A fair federated learning approach to reducing demographic bias in privacy-sensitive classiﬁcation models.

Critical Appraisal of Fairness Metrics in Clinical Predictive AI Fairﬂ: A fair federated learning approach to reducing demographic bias in privacy-sensitive classiﬁcation models

Reference 2024

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