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This paper is in the following e-collection/theme issue:

Machine Learning (2231) Artificial Intelligence (2350) Health Care Quality and Health Services Research (360) Decision Support for Health Professionals (1638) Clinical Informatics (1446)

Published on in Vol 13 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/73960, first published .
An Artificial Intelligence–Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study

An Artificial Intelligence–Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study

An Artificial Intelligence–Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study

Authors of this article:

Orhun Vural1 Author Orcid Image ;   Bunyamin Ozaydin2, 3 Author Orcid Image ;   Khalid Y Aram4 Author Orcid Image ;   James Booth5 Author Orcid Image ;   Brittany Freeman Lindsey6 Author Orcid Image ;   Abdulaziz Ahmed2, 3 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Vural O, Ozaydin B, Booth J, Lindsey B, Ahmed A. Deep Learning-Based Forecasting of Boarding Patient Counts to Address Emergency Department Overcrowding. Informatics 2025;12(3):95 View
  2. Su P, Shih H, Xu J. Rapid Liver Fibrosis Evaluation Using the UNet-ResNet50-32 × 4d Model in Magnetic Resonance Elastography: Retrospective Study. JMIR Medical Informatics 2025;13:e80351 View

Conference Proceedings

  1. Lalithadevi B, Prasanna J, Sri J. 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC). Early Risk Prediction for Emergency Department Triage using Machine Learning Techniques View