Patient Event Sequences for Predicting Hospitalization Length of Stay

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

*Kontaktforfatter

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

3 Citationer (Scopus)

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.
OriginalsprogEngelsk
TitelArtificial Intelligence in Medicine : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings
RedaktørerJose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker
Antal sider6
ForlagSpringer
Publikationsdato7 jun. 2023
Sider51-56
ISBN (Trykt)978-3-031-34343-8
ISBN (Elektronisk)978-3-031-34344-5
DOI
StatusUdgivet - 7 jun. 2023
BegivenhedInternational Conference on Artificial Intelligence in Medicine - Bernardin Congress Centre, Portoroz, Slovenien
Varighed: 12 jun. 202315 jun. 2023
https://aime23.aimedicine.info/

Konference

KonferenceInternational Conference on Artificial Intelligence in Medicine
LokationBernardin Congress Centre
Land/OmrådeSlovenien
ByPortoroz
Periode12/06/202315/06/2023
Internetadresse
NavnLecture Notes in Computer Science (LNCS)
Vol/bindLNCS 13897
ISSN0302-9743

Fingeraftryk

Dyk ned i forskningsemnerne om 'Patient Event Sequences for Predicting Hospitalization Length of Stay'. Sammen danner de et unikt fingeraftryk.

Citationsformater