Forecasting pediatric emergency department arrivals: Evaluating the role of exogenous variables using deep learning models
Recommended Citation
Etu EE, Larot J, Etu K, Emakhu J, Masoud S, Tenebe I, Huang G, Gunaga S, and Miller J. Forecasting pediatric emergency department arrivals: Evaluating the role of exogenous variables using deep learning models. Intelligence-Based Medicine 2025;12.
Document Type
Article
Publication Date
1-1-2025
Publication Title
Intelligence-Based Medicine
Keywords
Attention-based neural network, Children, Deep learning, Forecasting, Hospital, Long short-term memory, Pediatric emergency department
Abstract
Background: Forecasting pediatric emergency department (ED) demand remains a critical challenge in healthcare operations. This study aimed to identify exogenous variables influencing pediatric ED visits and evaluate the performance of different forecasting models.
Method: Using a retrospective observational design, we analyzed 192,347 pediatric ED visits across nine hospitals in Southeast Michigan between 2017 and 2019. Patient data were aggregated into daily arrival counts and enriched with exogenous variables such as weather, air quality, pollen, calendar, Google search trends, and chief complaints. Feature selection was performed using XGBoost and SHapley Additive exPlanations to identify the most influential predictors. Three forecasting models were developed: a Naïve baseline, Long Short-Term Memory (LSTM), and an attention-based neural network. The models were evaluated across 1-day, 7-day, and 14-day forecasting horizons using mean absolute percentage error (MAPE) and R2 metrics.
Results: LSTM and attention-based model significantly outperformed the Naïve baseline across all horizons. The LSTM model incorporating calendar data achieved the best 1-day forecast (MAPE: 8.71 %, R2: 0.67). For 7-day forecasts, the attention-based model using chief complaint data performed best (MAPE: 9.18 %, R2: 0.57). At 14 days, the attention-based model without exogenous inputs outperformed most LSTM variants, reflecting superior performance in long-range forecasting. Among exogenous variables, calendar and chief complaint data added the most predictive value, while Google Trends and pollen data introduced noise and diminished model performance.
Conclusion: Combining deep learning architectures with selected external data improves pediatric ED arrival forecasting. From an operational perspective, such forecasts can support more efficient staffing, reduce wait times, and mitigate ED crowding.
Volume
12
First Page
100313
