A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding

Emergency Departments (EDs) are a fundamental element of the Portuguese National Health Service, serving as an entry point for users with diverse and very serious medical problems. Due to the inherent characteristics of the ED, forecasting the number of patients using the services is particularly challenging. And a mismatch between affluence and the number of medical professionals can lead to a decrease in the quality of the services provided and create problems that have repercussions for the entire hospital, with the requisition of healthcare workers from other departments and the postponement of surgeries. ED overcrowding is driven, in part, by non-urgent patients that resort to emergency services despite not having a medical emergency, representing almost half of the total number of daily patients. This paper describes a novel deep learning architecture, the Temporal Fusion Transformer, that uses calendar and time-series covariates to forecast prediction intervals and point predictions for a 4-week period. We have concluded that patient volume can be forecast with a Mean Absolute Percentage Error (MAPE) of 9.87% for Portugal’s Health Regional Areas (HRA) and a Root Mean Squared Error (RMSE) of 178 people/day. The paper shows empirical evidence supporting the use of a multivariate approach with static and time-series covariates while surpassing other models commonly found in the literature.

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Bibtex:

@inproceedings{caldas2023temporal, title={A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding}, author={Caldas, Francisco M and Soares, Cl{\'a}udia}, booktitle={Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19--23, 2022, Proceedings, Part I}, pages={71--88}, year={2023}, organization={Springer}}