7 papers
Towards Fair Predictions: Group Conditional Concordance Index to Quantify Fairness in Time-to-Event Prognostication
Haoyuan Wang, Riddhiman Bhattacharya, Richardo Henao +3
Fairness metrics are essential for rigorously defining, quantifying, and mitigating biases in predictive models. While most existing metrics focus on binary classification tasks, f…
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…
Model-X Change-Point Detection of Conditional Distribution
Zhuofan Dong, Yiwen Huang, Yan Dong +5
The dynamic nature of many real-world systems can lead to temporal outcome model shifts, causing a deterioration in model accuracy and reliability over time. This requires change-p…
RELEAP: Reinforcement-Enhanced Label-Efficient Active Phenotyping for Electronic Health Records
Yang Yang, Kathryn I. Pollak, Bibhas Chakraborty +3
Objective: Electronic health record (EHR) phenotyping often relies on noisy proxy labels, which undermine the reliability of downstream risk prediction. Active learning can reduce…
SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
Wangxuan Fan, Siqi Li, Doudou Zhou +4
Explainable artificial intelligence (XAI) is essential for trustworthy machine learning (ML), particularly in high-stakes domains such as healthcare and finance. Shapley value (SV)…
Robust Mixture Models for Algorithmic Fairness Under Latent Heterogeneity
Siqi Li, Molei Liu, Ziye Tian +2
Standard machine learning models optimized for average performance often fail on minority subgroups and lack robustness to distribution shifts. This challenge worsens when subgroup…