Mitigating Bias in Opportunistic Screening for MACE with Causal Reasoning.

ABSTRACT: Mitigating population drift is vital for developing robust AI models for clinical use. While current methodologies focus on reducing demographic bias in disease predictions, they overlook the significant impact of chronic comorbidities. Addressing these complexities is essential to enhance predictive accuracy and reliability across diverse patient demographics, ultimately improving healthcare outcomes. We propose a causal reasoning framework to address selection bias in opportunistic screening for 1-year composite MACE risk using chest X-ray images. Training in high-risk primarily Caucasian patients (43% MACE event), the model was evaluated in a lower-risk emergency department setting (12.8% MACE event) and a relatively lower-risk external Asian patient population (23.81% MACE event) to as

SUBMITTER: Pi J 

PROVIDER: S-EPMC12768338 | biostudies-literature | 2025 May

REPOSITORIES: biostudies-literature