Emergency Department Length of Stay Classification Based on Ensemble Methods and Rule Extraction.

Journal: Studies in health technology and informatics
PMID: 39176843

Abstract

This study employs machine learning techniques to identify factors that influence extended Emergency Department (ED) length of stay (LOS) and derives transparent decision rules to complement the results. Leveraging a comprehensive dataset, Gradient Boosting exhibited marginally superior predictive performance compared to Random Forest for LOS classification. Notably, variables like triage acuity and the Elixhauser Comorbidity Index (ECI) emerged as robust predictors. The extracted rules optimize LOS stratification and resource allocation, demonstrating the critical role of data-driven methodologies in improving ED workflow efficiency and patient care delivery.

Authors

  • Waqar Aziz
    Biomedical Engineering Research Centre, University of Cyprus, Cyprus.
  • Andria Nicalaou
    Biomedical Engineering Research Centre, University of Cyprus, Cyprus.
  • Charithea Stylianides
    CYENS Centre of Excellence, Nicosia, Cyprus.
  • Andreas Panayides
    CYENS Centre of Excellence, Nicosia, Cyprus.
  • Antonis Kakas
    Department of Computer Science and Biomedical Engineering Research Centre, University of Cyprus, Nicosia, Cyprus.
  • Efthyvoulous Kyriacou
    Cyprus Technical University, Cyprus.
  • Constantinos Pattichis
    Biomedical Engineering Research Centre, University of Cyprus, Cyprus.

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