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

doi:10.1007/978-3-031-34344-5_7

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 proceeding › Konferenceartikel i proceeding › Forskning › peer review

4 Citationer (Scopus)

111 Downloads (Pure)

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.

Originalsprog	Engelsk
Titel	Artificial Intelligence in Medicine : 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings
Redaktører	Jose M. Juarez, Mar Marcos, Gregor Stiglic, Allan Tucker
Antal sider	6
Forlag	Springer
Publikationsdato	7 jun. 2023
Sider	51-56
ISBN (Trykt)	978-3-031-34343-8
ISBN (Elektronisk)	978-3-031-34344-5
DOI	https://doi.org/10.1007/978-3-031-34344-5_7
Status	Udgivet - 7 jun. 2023
Begivenhed	International Conference on Artificial Intelligence in Medicine - Bernardin Congress Centre, Portoroz, Slovenien Varighed: 12 jun. 2023 → 15 jun. 2023 https://aime23.aimedicine.info/

Konference

Konference	International Conference on Artificial Intelligence in Medicine
Lokation	Bernardin Congress Centre
Land/Område	Slovenien
By	Portoroz
Periode	12/06/2023 → 15/06/2023
Internetadresse	https://aime23.aimedicine.info/

Navn	Lecture Notes in Computer Science (LNCS)
Vol/bind	LNCS 13897
ISSN	0302-9743

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10.1007/978-3-031-34344-5_7

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1 Afsluttet

Poul Due Jensen Professorate in Big Data and Artificial Intelligence
Hose, K. (PI (principal investigator)), Jendal, T. E. (Projektdeltager) & Hansen, E. R. (Projektdeltager)
01/11/2019 → 31/12/2025
Projekter: Projekt › Forskning

4 Citationer
1 Ph.d.-afhandling (grad tildelt)

Representing Health Data and Medical Knowledge for Deep Learning
Hansen, E. R., 2023, Aalborg Universitetsforlag. 163 s.
Publikation: Ph.d.-afhandling (grad tildelt)

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Citationsformater

Hansen, E. R., Nielsen, T. D., Larsen, T. M., Strausholm, M. N., Sagi, T., & Hose, K. (2023). Patient Event Sequences for Predicting Hospitalization Length of Stay. I J. M. Juarez, M. Marcos, G. Stiglic, & A. Tucker (red.), Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings (s. 51-56). Springer. https://doi.org/10.1007/978-3-031-34344-5_7

Hansen, Emil Riis ; Nielsen, Thomas Dyhre ; Larsen, Thomas Mulvad et al. / Patient Event Sequences for Predicting Hospitalization Length of Stay. Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings. red. / Jose M. Juarez ; Mar Marcos ; Gregor Stiglic ; Allan Tucker. Springer, 2023. s. 51-56 (Lecture Notes in Computer Science (LNCS), Bind LNCS 13897).

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title = "Patient Event Sequences for Predicting Hospitalization Length of Stay",

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. ",

keywords = "length of stay prediction, sequence models, transformers",

author = "Hansen, \{Emil Riis\} and Nielsen, \{Thomas Dyhre\} and Larsen, \{Thomas Mulvad\} and Strausholm, \{Mads Nibe\} and Tomer Sagi and Katja Hose",

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booktitle = "Artificial Intelligence in Medicine",

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Hansen, ER, Nielsen, TD, Larsen, TM, Strausholm, MN, Sagi, T & Hose, K 2023, Patient Event Sequences for Predicting Hospitalization Length of Stay. i JM Juarez, M Marcos, G Stiglic & A Tucker (red), Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings. Springer, Lecture Notes in Computer Science (LNCS), bind LNCS 13897, s. 51-56, International Conference on Artificial Intelligence in Medicine, Portoroz, Slovenien, 12/06/2023. https://doi.org/10.1007/978-3-031-34344-5_7

Patient Event Sequences for Predicting Hospitalization Length of Stay. / Hansen, Emil Riis; Nielsen, Thomas Dyhre; Larsen, Thomas Mulvad et al.
Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings. red. / Jose M. Juarez; Mar Marcos; Gregor Stiglic; Allan Tucker. Springer, 2023. s. 51-56 (Lecture Notes in Computer Science (LNCS), Bind LNCS 13897).

Publikation: Bidrag til bog/antologi/rapport/konference proceeding › Konferenceartikel i proceeding › Forskning › peer review

TY - GEN

T1 - Patient Event Sequences for Predicting Hospitalization Length of Stay

AU - Hansen, Emil Riis

AU - Nielsen, Thomas Dyhre

AU - Larsen, Thomas Mulvad

AU - Strausholm, Mads Nibe

AU - Sagi, Tomer

AU - Hose, Katja

PY - 2023/6/7

Y1 - 2023/6/7

N2 - 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.

AB - 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.

KW - length of stay prediction

KW - sequence models

KW - transformers

UR - https://www.scopus.com/pages/publications/85151086836

U2 - 10.1007/978-3-031-34344-5_7

DO - 10.1007/978-3-031-34344-5_7

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SN - 978-3-031-34343-8

T3 - Lecture Notes in Computer Science (LNCS)

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EP - 56

BT - Artificial Intelligence in Medicine

A2 - Juarez, Jose M.

A2 - Marcos, Mar

A2 - Stiglic, Gregor

A2 - Tucker, Allan

PB - Springer

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Y2 - 12 June 2023 through 15 June 2023

ER -

Hansen ER, Nielsen TD, Larsen TM, Strausholm MN, Sagi T , Hose K. Patient Event Sequences for Predicting Hospitalization Length of Stay. I Juarez JM, Marcos M, Stiglic G, Tucker A, red., Artificial Intelligence in Medicine: 21st International Conference on Artificial Intelligence in Medicine, AIME 2023, Portorož, Slovenia, June 12–15, 2023, Proceedings. Springer. 2023. s. 51-56. (Lecture Notes in Computer Science (LNCS), Bind LNCS 13897). doi: 10.1007/978-3-031-34344-5_7

Patient Event Sequences for Predicting Hospitalization Length of Stay

Abstract

Konference

FN’s Verdensmål

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AUB Link

Andre filer og links

Fingeraftryk

Projekter

Poul Due Jensen Professorate in Big Data and Artificial Intelligence

Publikation

Representing Health Data and Medical Knowledge for Deep Learning

Citationsformater