Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy

Annelore Jellemijn Pieters; Stefan Schlobach

doi:10.1007/978-3-031-20627-6_8

Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy

Annelore Jellemijn Pieters, Stefan Schlobach^*

^*Corresponding author for this work

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

129 Downloads (Pure)

Abstract

This research investigates in how far AI methods can support the prediction of bed occupancy in hospital units based on individual patient data. We combine process mining and a Deep Spatial-Temporal Graph Modeling algorithm and show that this improves the performance of the prediction over existing approaches. To improve the model even more it is extended with knowledge available from patient records, like the day of the week, the time of the day, whether it is a vacation day or not and the amount of emergency cases per data point.

Original language	English
Title of host publication	Health Information Science
Subtitle of host publication	11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings
Editors	Agma Traina, Hua Wang, Yong Zhang, Siuly Siuly, Rui Zhou, Lu Chen
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	76-87
Number of pages	12
ISBN (Electronic)	9783031206276
ISBN (Print)	9783031206269
DOIs	https://doi.org/10.1007/978-3-031-20627-6_8
Publication status	Published - 2022
Event	11th International Conference on Health Information Science, HIS 2022 - Virtual, Online Duration: 28 Oct 2022 → 30 Oct 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13705 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th International Conference on Health Information Science, HIS 2022
City	Virtual, Online
Period	28/10/22 → 30/10/22

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Access to Document

10.1007/978-3-031-20627-6_8

Combining Process Mining and Time Series Forecasting to Predict Hospital Bed OccupancyFinal published version, 1.16 MBLicence: Article 25fa Dutch Copyright Act

Persistent URL (handle)

Persistent link

Cite this

Pieters, A. J., & Schlobach, S. (2022). Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy. In A. Traina, H. Wang, Y. Zhang, S. Siuly, R. Zhou, & L. Chen (Eds.), Health Information Science: 11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings (pp. 76-87). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13705 LNCS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-20627-6_8

Pieters, Annelore Jellemijn ; Schlobach, Stefan. / Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy. Health Information Science: 11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings. editor / Agma Traina ; Hua Wang ; Yong Zhang ; Siuly Siuly ; Rui Zhou ; Lu Chen. Springer Science and Business Media Deutschland GmbH, 2022. pp. 76-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{0b81af0d27914d63ac726f2ad2d5f8d4,

title = "Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy",

abstract = "This research investigates in how far AI methods can support the prediction of bed occupancy in hospital units based on individual patient data. We combine process mining and a Deep Spatial-Temporal Graph Modeling algorithm and show that this improves the performance of the prediction over existing approaches. To improve the model even more it is extended with knowledge available from patient records, like the day of the week, the time of the day, whether it is a vacation day or not and the amount of emergency cases per data point.",

author = "Pieters, {Annelore Jellemijn} and Stefan Schlobach",

note = "Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 11th International Conference on Health Information Science, HIS 2022 ; Conference date: 28-10-2022 Through 30-10-2022",

year = "2022",

doi = "10.1007/978-3-031-20627-6_8",

language = "English",

isbn = "9783031206269",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer Science and Business Media Deutschland GmbH",

pages = "76--87",

editor = "Agma Traina and Hua Wang and Yong Zhang and Siuly Siuly and Rui Zhou and Lu Chen",

booktitle = "Health Information Science",

address = "Germany",

}

Pieters, AJ & Schlobach, S 2022, Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy. in A Traina, H Wang, Y Zhang, S Siuly, R Zhou & L Chen (eds), Health Information Science: 11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13705 LNCS, Springer Science and Business Media Deutschland GmbH, pp. 76-87, 11th International Conference on Health Information Science, HIS 2022, Virtual, Online, 28/10/22. https://doi.org/10.1007/978-3-031-20627-6_8

Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy. / Pieters, Annelore Jellemijn; Schlobach, Stefan.
Health Information Science: 11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings. ed. / Agma Traina; Hua Wang; Yong Zhang; Siuly Siuly; Rui Zhou; Lu Chen. Springer Science and Business Media Deutschland GmbH, 2022. p. 76-87 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13705 LNCS).

Research output: Chapter in Book / Report / Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy

AU - Pieters, Annelore Jellemijn

AU - Schlobach, Stefan

PY - 2022

Y1 - 2022

N2 - This research investigates in how far AI methods can support the prediction of bed occupancy in hospital units based on individual patient data. We combine process mining and a Deep Spatial-Temporal Graph Modeling algorithm and show that this improves the performance of the prediction over existing approaches. To improve the model even more it is extended with knowledge available from patient records, like the day of the week, the time of the day, whether it is a vacation day or not and the amount of emergency cases per data point.

AB - This research investigates in how far AI methods can support the prediction of bed occupancy in hospital units based on individual patient data. We combine process mining and a Deep Spatial-Temporal Graph Modeling algorithm and show that this improves the performance of the prediction over existing approaches. To improve the model even more it is extended with knowledge available from patient records, like the day of the week, the time of the day, whether it is a vacation day or not and the amount of emergency cases per data point.

UR - http://www.scopus.com/inward/record.url?scp=85142610672&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85142610672&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-20627-6_8

DO - 10.1007/978-3-031-20627-6_8

M3 - Conference contribution

AN - SCOPUS:85142610672

SN - 9783031206269

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 76

EP - 87

BT - Health Information Science

A2 - Traina, Agma

A2 - Wang, Hua

A2 - Zhang, Yong

A2 - Siuly, Siuly

A2 - Zhou, Rui

A2 - Chen, Lu

PB - Springer Science and Business Media Deutschland GmbH

T2 - 11th International Conference on Health Information Science, HIS 2022

Y2 - 28 October 2022 through 30 October 2022

ER -

Pieters AJ, Schlobach S. Combining Process Mining and Time Series Forecasting to Predict Hospital Bed Occupancy. In Traina A, Wang H, Zhang Y, Siuly S, Zhou R, Chen L, editors, Health Information Science: 11th International Conference, HIS 2022, Virtual Event, October 28–30, 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 76-87. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Epub 2022 Oct 25. doi: 10.1007/978-3-031-20627-6_8