Affiliation:
1. Federal University of Rio Grande do Sul
Abstract
Abstract
Background
Emergency department (ED) overcrowding is an important problem in many countries. Accurate predictions of patient arrivals in EDs can serve as a management baseline for better allocation of staff and medical resources. In this article, we investigate the use of calendar and meteorological predictors, as well as feature engineered variables, to forecast daily patient arrivals using datasets from eleven different EDs across 3 countries.
Methods
Six machine learning algorithms were tested, considering forecasting horizons of 7 and 45 days ahead. Tuning of hyperparameters was performed using a grid-search with cross-validation. Algorithms' performance was evaluated using 5-fold cross-validation and four performance metrics.
Results
The eXtreme Gradient Boosting (XGBoost) achieved better performance considering the two prediction horizons compared to other models, also outperforming results reported in past studies on ED arrival prediction. This is also the first study to utilize Light Gradient Boosting Machine (LightGBM), Support Vector Machine with Radial Basis Function (SVM-RBF) and Neural Network Autoregression (NNAR) for predicting patient arrivals at EDs.
Conclusion
The Random Forest (RF) variable selection and grid-search methods improved the accuracy of the algorithms tested. Our study innovates by using feature engineering to predict patient arrivals in EDs.
Publisher
Research Square Platform LLC
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