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Authors: Alexander Winter 1 ; 2 ; Mattis Hartwig 3 ; 2 and Toralf Kirsten 1

Affiliations: 1 Department of Medical Data Science, Leipzig University, Germany ; 2 singularIT GmbH, 04109 Leipzig, Germany ; 3 German Research Center for Artificial Intelligence, 23562 Lübeck, Germany

Keyword(s): Length of Stay Prediction, Emergency Deparment, MIMIC-IV, CatBoost Architecture.

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.

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Paper citation in several formats:
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) - HEALTHINF; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 124-131. DOI: 10.5220/0011671700003414

@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) - HEALTHINF},
year={2023},
pages={124-131},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011671700003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - HEALTHINF
TI - Predicting Hospital Length of Stay of Patients Leaving the Emergency Department
SN - 978-989-758-631-6
IS - 2184-4305
AU - Winter, A.
AU - Hartwig, M.
AU - Kirsten, T.
PY - 2023
SP - 124
EP - 131
DO - 10.5220/0011671700003414
PB - SciTePress

- Science and Technology Publications, Lda.
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