Record link: 
http://hdl.handle.net/20.500.12708/193128
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Title: 
Patient Event Sequences for Predicting Hospitalization Length of Stay
en
Citation: 
Hansen, E. R., Nielsen, T. D., Mulvad, T., Strausholm, M. N., Sagi, T., & Hose, K. (2023). Patient Event Sequences for Predicting Hospitalization Length of Stay. In J. M. Juarez, M. Marcos, G. Stiglic, & A. Tucker (Eds.), Artificial Intelligence in Medicine : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings (pp. 51–56). Springer. https://doi.org/10.1007/978-3-031-34344-5_7
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Publisher DOI: 
10.1007/978-3-031-34344-5_7
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Publication Type: 
Inproceedings - Full-Paper Contribution
en
Language: 
English
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Authors: 
Hansen, Emil Riis 
Nielsen, Thomas Dyhre 
Mulvad, Thomas 
Strausholm, Mads Nibe 
Sagi, Tomer 
Hose, Katja 
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Organisational Unit: 
E192-02 - Forschungsbereich Databases and Artificial Intelligence 
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Published in: 
Artificial Intelligence in Medicine : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings 
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ISBN: 
978-3-031-34343-8
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DOI of the book: 
10.1007/978-3-031-34344-5_7
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Date (published): 
5-Jun-2023
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Event name: 
AIME 2023 
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Event date: 
12-Jun-2023 - 15-Jun-2023
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Event place: 
Portoroz, Slovenia
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Number of Pages: 
6
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Publisher: 
Springer
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Peer reviewed: 
Yes
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Keywords: 
length of stay prediction (LOS); transformers; sequence models; healthcare; Medic-BERT (M-BERT); Machine Learning
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Abstract: 
Predicting patients’ hospital length of stay (LOS) is essential for improving resource allocation and supporting decision-making in healthcare organizations. This paper proposes a novel transformer-based model, termed Medic-BERT (M-BERT), for predicting LOS by modeling patient information as sequences of events. We performed empirical experiments on a cohort of 48k emergency care patients from a large Danish hospital. Experimental results show that M-BERT can achieve high accuracy on a variety of LOS problems and outperforms traditional non-sequence-based machine learning approaches.
en
Research Areas: 
Logic and Computation: 100%
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Science Branch: 
1020 - Informatik: 80%
1010 - Mathematik: 20%
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Appears in Collections:
Conference Paper

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