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.
|Title of host publication||Artificial Intelligence in Medicine - 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Proceedings|
|Editors||Jose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||6|
|State||Published - 2023|
|Event||21st International Conference on Artificial Intelligence in Medicine, AIME 2023 - Portoroz, Slovenia|
Duration: 12 Jun 2023 → 15 Jun 2023
|Name||Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)|
|Conference||21st International Conference on Artificial Intelligence in Medicine, AIME 2023|
|Period||12/06/23 → 15/06/23|
Bibliographical noteFunding Information:
This study received funding from the Region North Denmark Health Innovation Foundation. This study is also supported by the Poul Due Jensen Foundation.
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
- length of stay prediction
- sequence models
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science (all)