What is it about?

This chapter proposes a decision-support tool for emergency departments that classifies patients before formal triage begins. The model uses information available at registration, such as demographic data, symptoms, and vital signs, to help identify urgent cases earlier. The study uses an interpretable decision-tree model trained with emergency visit data labeled under the Korean Triage and Acuity Scale, with special attention to reducing the risk of under-triage.

Featured Image

Why is it important?

Emergency departments often face crowding, long waiting times, and pressure to quickly identify urgent patients. This work is important because it explores how interpretable machine learning can complement, not replace, clinical triage. By prioritizing sensitivity and using variables that align with clinical expectations, the approach may support safer patient prioritization and better operational flow. The findings should be read cautiously because they come from a specific dataset and require further validation before real-world deployment.

Perspectives

This chapter is personally meaningful because I had the pleasure of working with Andrea, an industrial engineer who is now a professional, and collaborating with Dilan for the first time. For me, it is truly significant to see students, emerging researchers, and recent master’s graduates begin to publish and participate in academic conferences. Beyond the technical contribution, this work represents a shared learning process and a step toward building future research collaborations in healthcare analytics, operations research, and decision-support systems.

Mr Leonardo Hernan Talero-Sarmiento
Universidad Autonoma de Bucaramanga

Read the Original

This page is a summary of: Pre-admission in Emergency Departments: Algorithms for Triage Classification, January 2026, Springer Science + Business Media,
DOI: 10.1007/978-3-032-19656-9_49.
You can read the full text:

Read

Resources

Contributors

The following have contributed to this page