Artificial intelligence, and in particular Graph Neural Networks (GNNs), have shown enormous potential in the analysis of complex clinical data. Thanks to their ability to model relationships between variables, GNNs represent a significant evolution compared to traditional models, enabling better interpretation of medical information and supporting data-driven decision-making in complex contexts such as emergency medicine.

The application of GNNs to clinical triage and to the prediction of length of stay can improve clinical efficiency by optimizing resource allocation and patient management. This observational study aims to evaluate the accuracy of predictions with respect to real clinical data, contributing to the development of advanced predictive tools to support healthcare decision-making processes.

Inclusion Criteria:

• all

Exclusion Criteria:

• Patients labelled with red codes and serious injuries.

All individuals who request access to the ER will be duly considered.