ArticleBMC emergency medicine2025
Predicting emergency department admissions using a machine-learning algorithm: a proof of concept with retrospective study.
Article in BMC emergency medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Perception and challenges of artificial intelligence (AI) in Emergency Medicine: A multi-country study in Sub-Saharan Africa.African journal of emergency medicine : Revue africaine de la medecine d'urgence · 2026Article
- Artificial Intelligence Models for Predicting Triage in Emergency Departments: Seven-Month Retrospective Comparative Study of Natural Language Processing, Large Language Model, and Joint Embedding Predictive Architectures.JMIR medical informatics · 2026Article
- Routine laboratory data for predicting 30-day emergency department revisits: the AXIS-2 risk score.BMC emergency medicine · 2026Article
- Evaluating an AI Decision Support System for the Emergency Department: Retrospective Study.JMIR AI · 2026Article
- Time-series-based forecasting of accident-related referrals to Maharaj Nakorn Chiang Mai Hospital, Northern Thailand, during each year and especially the "Seven dangerous Days" periods.BMC public health · 2026Article
- Research trends and ethical perspectives on explainable artificial intelligence in emergency medicine: a bibliometric analysis.Scandinavian journal of trauma, resuscitation and emergency medicine · 2026Review
- Evaluation of Large Language Models as Emergency Department Revisit Predictors.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2026Article
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
introductionOvercrowding in emergency departments (ED) is a major public health issue, leading to increased workload and exhaustion for the teams, resulting poor outcomes. It seems interesting to be able to predict the admissions of patients in the ED.
aimThe main objective of this study was to build and test a prediction tool for ED admissions using artificial intelligence.
methodsWe performed a retrospective multicenter study in two French ED from January 1st, 2010 to December 31st, 2019.We tested several machine learning algorithms and compared the results.
resultsThe arrival and departure times from the ED of 2 hospitals were collected from all consultations during the study period, then grouped into 87 600 one-hour slots. Through the development of two models (one for each location), we found that the XGBoost method with hyperparameter adaptations was the best, suggesting that the studied data could be predicted (mean absolute error) at 2.63 for Hospital 1 and 2.64 for Hospital 2).
conclusionsThis study ran the construction and validation of a powerful tool for predicting ED admissions in 2 French ED. This type of tool should be integrated into the overall organization of an ED, to optimize the resources of healthcare professionals.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.