Predicting waiting times in A&E is a critical tool for controlling the flow of patients in the department. The most used method (rolling average) does not account for the complex context of the A&E. Using retrospective data of patients visiting an A&E service from 2017 to 2019 (pre-pandemic). An AI-enabled method is used to predict waiting times in this study. A random forest and XGBoost regression methods were trained and tested to predict the time to discharge before the patient arrived at the hospital. When applying the final models to the 68,321 observations and using the complete set of features, the random forest algorithm’s performance measurements are RMSE=85.31 and MAE=66.71. The XGBoost model obtained a performance of RMSE=82.66 and MAE=64.31. The approach might be a more dynamic method to predict waiting times.
IOS Press, Inc.
6751 Tepper Drive
Clifton, VA 20124
Tel.: +1 703 830 6300
Fax: +1 703 830 2300 email@example.com
(Corporate matters and books only) IOS Press c/o Accucoms US, Inc.
For North America Sales and Customer Service
West Point Commons
Lansdale PA 19446
Tel.: +1 866 855 8967
Fax: +1 215 660 5042 firstname.lastname@example.org