A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models 事件

A Practical Upper Bound on Selection Bias Effects in Medical Prediction Models arXiv:2606.00563v1 Announce Type: cross Abstract: Selection bias is a common and often unavoidable aspect of real-world data that challenges the generalizability of machine learning models. When models trained on biased data are deployed in the broader target population, poor model generalization may lead to real harm, particularly in high-risk settings such as healthcare. This risk highlights the need for practition