5 papers
Distributionally Robust Transfer Learning with Structurally Missing Covariates, with Application to Cross-National Cardiac Arrest Prediction
Siqi Li, Chuan Hong, Ziye Tian +9
Deploying clinical prediction models across healthcare systems often fails when key training covariates are unavailable at deployment and labeled outcomes are limited in the target…
Equitable Survival Prediction: A Fairness-Aware Survival Modeling (FASM) Approach
Mingxuan Liu, Yilin Ning, Haoyuan Wang +5
As machine learning models become increasingly integrated into healthcare, structural inequities and social biases embedded in clinical data can be perpetuated or even amplified by…
Gender Bias in Large Language Models for Healthcare: Assignment Consistency and Clinical Implications
Mingxuan Liu, Yuhe Ke, Wentao Zhu +9
The integration of large language models (LLMs) into healthcare holds promise to enhance clinical decision-making, yet their susceptibility to biases remains a critical concern. Ge…
seeBias: A Comprehensive Tool for Assessing and Visualizing AI Fairness
Yilin Ning, Yian Ma, Mingxuan Liu +2
Fairness in artificial intelligence (AI) prediction models is increasingly emphasized to support responsible adoption in high-stakes domains such as health care and criminal justic…
FairFML: Fair Federated Machine Learning with a Case Study on Reducing Gender Disparities in Cardiac Arrest Outcome Prediction
Siqi Li, Qiming Wu, Xin Li +10
Objective: Mitigating algorithmic disparities is a critical challenge in healthcare research, where ensuring equity and fairness is paramount. While large-scale healthcare data exi…