Work

Multi-task learning with dynamic re-weighting to achieve fairness in healthcare predictive modeling

Year: 2023

Type: article

Source: Journal of Biomedical Informatics

DOI: 10.1016/j.jbi.2023.104399

Authors: Can Li, Sirui Ding, Na Zou, Xia Hu, Xiaoqian Jiang, Kai Zhang

Institutions: The University of Texas Health Science Center at Houston, Texas A&M University, Rice University

Language: English

Topic: Ethics and Social Impacts of AI

Subfield: Safety Research

Field: Social Sciences

Domain: Social Sciences

SDGs: Reduced inequalities, Good health and well-being

Open Access status: bronze

Funders: National Science Foundation, Cancer Prevention and Research Institute of Texas, University of Texas Health Science Center at Houston, National Institutes of Health, National Institute on Aging, U.S. National Library of Medicine, National Center for Advancing Translational Sciences

Awards: CAREER: Exploring and Exploiting Data-Centric Modeling for Fairness in Machine Learning, Recruitment of Rising Stars, Open Health Natural Language Processing Collaboratory, Finding combinatorial drug repositioning therapy for Alzheimer's disease and related dementias, G5388406022, FAI: Towards Fairness in Deep Neural Networks with Learning Interpretation, Collaborative Research: SaTC: CORE: Medium: PREMED: Privacy-Preserving and Robust Computational Phenotyping using Multisite EHR Data, III: Medium: Collaborative Research: Towards Effective Interpretation of Deep Learning: Prediction, Representation, Modeling and Utilization, Decentralized differentially-private methods for dynamic data release and analysis

OpenAlex ID: W4377101068

FWCI: 7.256

Cites: 45

Cited by: 29

Related to: 10