Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning

1 August 2022

Papers citing "Disparate Censorship & Undertesting: A Source of Label Bias in Clinical Machine Learning"

6 / 6 papers shown

Title
To which reference class do you belong? Measuring racial fairness of reference classes with normative modeling S. Rutherford T. Wolfers Charlotte J. Fraza Nathaniel G. Harrnet Christian F. Beckmann H. Ruhé A. Marquand CML 34 2 0 26 Jul 2024
From Biased Selective Labels to Pseudo-Labels: An Expectation-Maximization Framework for Learning from Biased Decisions Trenton Chang Jenna Wiens 24 0 0 27 Jun 2024
Leveraging Generative AI for Clinical Evidence Summarization Needs to Ensure Trustworthiness Gongbo Zhang Qiao Jin Denis Jered McInerney Yong Chen Fei Wang ... Mor Peleg Byron C. Wallace Zhiyong Lu Chunhua Weng Yifan Peng 28 18 0 19 Nov 2023
Leveraging an Alignment Set in Tackling Instance-Dependent Label Noise Donna Tjandra Jenna Wiens NoLa 14 3 0 10 Jul 2023
Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods Raphael Poulain Mirza Farhan Bin Tarek Rahmatollah Beheshti FedML 16 20 0 19 May 2023
MOTOR: A Time-To-Event Foundation Model For Structured Medical Records E. Steinberg Jason Alan Fries Yizhe Xu N. Shah OOD AI4TS 23 13 0 09 Jan 2023