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Predicting Emergency Department Patient Arrivals at Hospitals Using Machine Learning Techniques.

Abdulmajeed M Alenezi¹, Mahmoud Sameh¹, Meshal Aljohani¹

¹College of Engineering, Islamic University of Madinah, Madinah 42351, Saudi Arabia.

Healthcare (Basel, Switzerland)

|May 13, 2026

Summary

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Accurate hourly Emergency Department (ED) arrival predictions can be achieved using engineered temporal features with linear or sequence models. This aids in optimizing hospital resource management and staffing decisions.

Area of Science:

Healthcare Operations Research
Machine Learning Applications
Time Series Forecasting

Background:

Emergency Departments (EDs) experience significant operational challenges due to unpredictable patient volumes and overcrowding.
Accurate short-term demand forecasting is essential for efficient ED staffing and resource allocation.
Minimizing patient entry delays requires precise hourly arrival predictions.

Purpose of the Study:

To develop and evaluate a forecasting framework for hourly ED patient arrivals.
To compare the performance of six different forecasting models, including machine learning approaches.
To assess the impact of engineered temporal features on prediction accuracy.

Main Methods:

Utilized de-identified hourly patient arrival data from an ED in Madinah, Saudi Arabia (January-November 2024).

Keywords:

emergency department forecasting machine learning patient arrivals time series

Related Experiment Videos

Engineered 183 features including time encodings, holiday indicators, autoregressive lags, and volatility measures.

Compared Seasonal Naive, ETS, Ridge Regression, LightGBM, TCN, and LSTM models using Bayesian optimization and an asymmetric loss function.

Main Results:

Ridge Regression yielded the lowest Mean Absolute Error (MAE) at 3.75 (R² = 0.52).
Hybrid TCN and LSTM models showed comparable performance (MAE 3.80 and 3.85).
All evaluated machine learning models significantly outperformed baseline methods (ETS, Seasonal Naive).

Conclusions:

Combining engineered temporal features with linear or sequence models provides accurate hourly ED arrival forecasts.
The operational sufficiency of forecast accuracy for staffing requires site-specific clinical validation.
Further research is needed to integrate these forecasting tools into real-world ED operations.