Interpretable machine learning models for prolonged Emergency Department wait time prediction.

Journal: BMC health services research
PMID: 40102847

Abstract

OBJECTIVE: Prolonged Emergency Department (ED) wait times lead to diminished healthcare quality. Utilizing machine learning (ML) to predict patient wait times could aid in ED operational management. Our aim is to perform a comprehensive analysis of ML models for ED wait time prediction, identify key feature importance and associations with prolonged wait times, and interpret prediction model clinical relevance among ED patients.

Authors

  • Hao Wang
    Department of Cardiology, Second Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Nethra Sambamoorthi
    Masters in Data Science Program, School of Professional Studies, Northwestern University, Chicago, IL 60201, USA.
  • Devin Sandlin
    Department of Emergency Medicine, John Peter Smith Health Network, Integrative Emergency Services, 1500 S. Main St., Fort Worth, TX, 76104, USA.
  • Usha Sambamoorthi
    Department of Pharmacotherapy, College of Pharmacy, "Vashisht" Professor of Disparities, Health Education, Awareness & Research in Disparities (HEARD) Scholar, Texas Center for Health Disparities, University of North Texas Health Sciences Center, 3500 Camp Bowie Blvd, Fort Worth, TX, 76107, USA. Electronic address: usha.sambamoorthi@unthsc.edu.

Keywords

External Resources

Popular Topics
Recent Journals