Résumé

Emergency departments (EDs) face increasing
patient demand, leading to overcrowding and resource strain.
Accurate forecasting of ED visits is critical for optimizing
hospital operations and ensuring efficient resource allocation.
This paper proposes a hybrid model combining Variational
Autoencoder (VAE) and Gated Recurrent Unit (GRU) to enhance
patient flow predictions. The VAE extracts meaningful
latent features while handling missing data, whereas the GRU
captures complex temporal dependencies, improving forecasting
accuracy. Compared to traditional models such as LSTM,
GRU, and 1D CNN, our hybrid VAE-GRU model demonstrates
superior predictive performance. Experimental results, based
on real-world hospital data, highlight the model’s effectiveness
in reducing prediction errors and improving decision-making
in dynamic ED environments. Additionally, we compare the
proposed model with ARIMA-ML, emphasizing the tradeoffs
between computational efficiency and prediction accuracy.
The findings suggest that hybrid deep learning approaches
can significantly enhance healthcare resource management,
reducing patient waiting times and improving overall hospital
efficiency.

Résumé

Emergency departments (EDs) must quickly assess
and prioritize patients, especially during crises when demand

exceeds capacity. Traditional triage methods, such as the Jump-
START protocol for pediatric cases and the START (Simple

Triage and Rapid Treatment) method for adults, are commonly
used but may lack precision under high-pressure situations.
This paper proposes a hybrid approach combining ensemble
models—XGBoost, AdaBoost, and CatBoost—with synthetic data
augmentation using Generative Adversarial Networks (GANs) to
enhance triage accuracy for critically ill patients.
Models were trained on real-world ED data, including vital
signs, symptoms, medical history, and demographics. GANs
generated synthetic critical cases to address class imbalance,
improving model sensitivity to high-risk profiles.

Results show that GAN-augmented models outperform base-
line models, with CatBoost offering the best balance between

accuracy and computational efficiency. This approach improves
patient prioritization, reduces delays, and supports better clinical
decision-making in resource-limited environments.
Index Terms—Emergency Department (ED), Patient Triage,

Machine Learning (ML), AdaBoost, XGBoost, CatBoost, Genera-
tive Adversarial Networks (GANs), Urgency Classification, Crisis

Situations.