Patient Event Sequences for Predicting Hospitalization Length of Stay

Emil Riis Hansen, Thomas Dyhre Nielsen, Thomas Mulvad, Mads Nibe Strausholm, Tomer Sagi, Katja Hose

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationArtificial Intelligence in Medicine - 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Proceedings
EditorsJose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages6
ISBN (Print)9783031343438
StatePublished - 2023
Externally publishedYes
Event21st International Conference on Artificial Intelligence in Medicine, AIME 2023 - Portoroz, Slovenia
Duration: 12 Jun 202315 Jun 2023

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13897 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference21st International Conference on Artificial Intelligence in Medicine, AIME 2023

Bibliographical note

Funding Information:
This study received funding from the Region North Denmark Health Innovation Foundation. This study is also supported by the Poul Due Jensen Foundation.

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.


  • length of stay prediction
  • sequence models
  • transformers

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)


Dive into the research topics of 'Patient Event Sequences for Predicting Hospitalization Length of Stay'. Together they form a unique fingerprint.

Cite this