From Classical Machine Learning to Tabular Foundation Models: An Empirical Investigation of Robustness and Scalability Under Class Imbalance in Emergency and Critical Care

ArXi:2512.21602v2 Announce Type: replace-cross Millions of patients pass through emergency departments and intensive care units each year, where clinicians must make high-stakes decisions under time pressure and uncertainty. Machine learning could these decisions by predicting deterioration, guiding triage, and identifying rare but serious outcomes. Yet clinical tabular data are often highly imbalanced, biasing models toward majority classes. Building methods that are robust to imbalance and efficient enough for deployment remains a practical challenge.