ArticleBMC medical informatics and decision making2024
Enhanced forecasting of emergency department patient arrivals using feature engineering approach and machine learning.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Forecast-Driven Dynamic Physician Staffing in a Pediatric Emergency Department: A Prospective Quasi-Experimental Pilot Study.Journal of medical systems · 2026Article
- Predicting the Unpredictable: A Data-Driven Machine Learning Model for Emergency Department Waiting Room Surge Status.Mayo Clinic proceedings. Digital health · 2026Article
- Predicting Emergency Department Patient Arrivals at Hospitals Using Machine Learning Techniques.Healthcare (Basel, Switzerland) · 2026Article
- A hybrid grey wolf optimized eXtreme gradient boosting-based machine learning model for hospital pharmaceutical demand forecasting.Scientific reports · 2026Article
- Dynamic demand forecasting and capacity assessment for emergency care system: a hybrid time series and machine learning approach with evidence from Beijing.BMC health services research · 2026Article
- Enhancing patient admission efficiency through a hybrid cloud framework for medical record sharing.Scientific reports · 2026Article
- Deep learning to predict emergency department revisit using static and dynamic features (Deep Revisit): development and validation study.BioData mining · 2025Article
- Prospective comparison of econometric, machine learning, and foundation models for forecasting emergency department boarding patients.npj health systems · 2025Article
- Smart emergency care: a narrative review of predictive machine learning models.Annals of translational medicine · 2025Review
- An Artificial Intelligence-Based Framework for Predicting Emergency Department Overcrowding: Development and Evaluation Study.JMIR medical informatics · 2025Article
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- Artificial intelligence for severity triage based on conversations in an emergency department in Korea.Scientific reports · 2025Article
- Implementing a prediction driven framework for emergency department nurse staffing to optimize real time decisions.npj health systems · 2025Article
- Interpretable machine learning models for prolonged Emergency Department wait time prediction.BMC health services research · 2025Article
- Predicting triage of pediatric patients in the emergency department using machine learning approach.International journal of emergency medicine · 2025Article
- Mapping artificial intelligence models in emergency medicine: A scoping review on artificial intelligence performance in emergency care and education.Turkish journal of emergency medicineReview
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2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundEmergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of ED patient arrivals can help management to better allocate staff and medical resources. In this study, we investigate the use of calendar and meteorological predictors, as well as feature-engineered variables, to predict daily patient arrivals using datasets from eleven different EDs across three countries.
methodsSix machine learning (ML) algorithms were tested on forecasting horizons of 7 and 45 days. Three of them - Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF), and Neural Network Autoregression (NNAR) - were never before reported for predicting ED patient arrivals. Algorithms' hyperparameters were tuned through a grid-search with cross-validation. Prediction performance was assessed using fivefold cross-validation and four performance metrics.
resultsThe eXtreme Gradient Boosting (XGBoost) was the best-performing model on both prediction horizons, also outperforming results reported in past studies on ED arrival prediction. XGBoost and NNAR achieved the best performance in nine out of the eleven analyzed datasets, with MAPE values ranging from 5.03% to 14.1%. Feature engineering (FE) improved the performance of the ML algorithms.
conclusionAccuracy in predicting ED arrivals, achieved through the FE approach, is key for managing human and material resources, as well as reducing patient waiting times and lengths of stay.
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