2025

Conférence

11th International Conference on Control, Decision and Information Technologies

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.

Final_Version__Short_Optimizing_Emergency_Department_Patient_Flow_Forecasting__A_Hybrid_VAE_GRU_Model