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

Researchers evaluated seven machine learning model families on imbalanced clinical data from emergency and critical care units, finding that TabPFN v2.6 and TabICL performed best overall, with XGBoost also showing strong results across tasks. This study highlights the potential of tabular foundation models for efficient and robust decision support in healthcare settings, particularly where resources are limited.

Read the full article at arXiv cs.LG (ML)

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