Predicting Hospital Length of Stay of Patients Leaving the Emergency Department

Alexander Winter, Alexander Winter, Mattis Hartwig, Mattis Hartwig, Toralf Kirsten

2023

Abstract

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.

Paper Citation

in Harvard Style

Winter A., Hartwig M. and Kirsten T. (2023). Predicting Hospital Length of Stay of Patients Leaving the Emergency Department. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF; ISBN 978-989-758-631-6, SciTePress, pages 124-131. DOI: 10.5220/0011671700003414

in Bibtex Style

@conference{healthinf23,
author={Alexander Winter and Mattis Hartwig and Toralf Kirsten},
title={Predicting Hospital Length of Stay of Patients Leaving the Emergency Department},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF},
year={2023},
pages={124-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011671700003414},
isbn={978-989-758-631-6},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - Volume 5: HEALTHINF
TI - Predicting Hospital Length of Stay of Patients Leaving the Emergency Department
SN - 978-989-758-631-6
AU - Winter A.
AU - Hartwig M.
AU - Kirsten T.
PY - 2023
SP - 124
EP - 131
DO - 10.5220/0011671700003414
PB - SciTePress