Skip to main content Skip to main navigation


Predicting Hospital Length of Stay of Patients Leaving the Emergency Department

Alex Winter; Mattis Hartwig; Toralf Kirsten
In: Federico Cabitza; Ana Fred; Hugo Gamboa (Hrsg.). Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies - (Volume 5). International Conference on Health Informatics (HEALTHINF-2023), SciTePress, 2023.


In this paper, we aim to predict the patient’s length of stay (LOS) after they are dismissed from the emergency department and transferred to the next hospital unit. An accurate prediction has positive effects for patients, doctors and hospital administrators. We extract a dataset of 181,797 patients from the United States and perform a set of feature engineering steps. For the prediction we use a CatBoost regression architecture with a specifically implemented loss function. The results are compared with baseline models and results from related work on other use cases. With an average absolute error of 2.36 days in the newly defined use case of post ED LOS prediction, we outperform baseline models achieve comparable results to use cases from intensive care unit LOS prediction. The approach can be used as a new baseline for further improvements of the prediction.