FERI: A Multitask-based Fairness Achieving Algorithm with Applications to Fair Organ Transplantation
Year: 2023
Type: article
Abstract: mprovement in Multitask Learning (FERI) algorithm for fair predictions of graft failure risk in liver transplant patients. FERI constrains subgroup loss by balancing learning rates and preventing subgroup dominance in the training process. Our results show that FERI maintained high predictive accuracy with AUROC and AUPRC comparable to baseline models. More importantly, FERI demonstrated an ability to improve fairness without sacrificing accuracy. Specifically, for the gender, FERI reduced the demographic parity disparity by 71.74%, and for the age group, it decreased the equalized odds disparity by 40.46%. Therefore, the FERI algorithm advanced fairness-aware predictive modeling in healthcare and provides an invaluable tool for equitable healthcare systems.
Source: PubMed
Authors: Li, Can, Lai, Dejian, Jiang, Xiaoqian, Zhang, Kai
Language: English
Subfield: Safety Research
Field: Social Sciences
Domain: Social Sciences
Open Access status: green
Awards: G1067511982, G2221527151, iDASH: Integrating Data for Analysis, Anonymization, and Sharing, G2641166645, G287445649, G2877228074, G33291116, G3642029426, Open Health Natural Language Processing Collaboratory, G723192185, Accelerating Genomic Data Sharing and Collaborative Research with Privacy Protection, AIM-AI: an Actionable, Integrated and Multiscale genetic map of Alzheimer's disease via deep learning
OpenAlex ID: W4387928536