Forecasting ED Crowds: Can Machine Learning Predict Hospital Admissions?

New research explores the potential of machine learning models to accurately forecast daily arrivals in emergency departments, offering insights for improved resource allocation.







![Phi-SegNet employs bi-feature mask formers and attention-guided skip connections to integrate encoder features, then refines segmentation through phase supervision and reverse Fourier attention [latex] \mathcal{R}\mathcal{F} [/latex] modules-a spectral filtering approach designed to sharpen boundary localization despite the inevitable complexities of production deployment.](https://arxiv.org/html/2601.16064v1/Figures/total_architecture.jpg)