Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study

Annika L. Dubbeldam; István Ketykó; Renata M. de Carvalho; Felix Mannhardt

doi:10.1007/978-3-031-27815-0_27

Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study

Annika L. Dubbeldam (Corresponderende auteur)
, István Ketykó
, Renata M. de Carvalho
, Felix Mannhardt

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

2 Citaten (Scopus)

25 Downloads (Pure)

Samenvatting

Patients, when in a hospital, will go through a personalized treatment scheduled for many different reasons and with various outcomes. Furthermore, some patients and/or treatments require aftercare. Identifying the need for aftercare is crucial for improving the process of the patient and hospital. A late identification results in a patient staying longer than needed, occupying a bed that otherwise could serve another patient. In this paper, we will investigate to what extent events from the first hours of stay can help in predicting the need for aftercare. For that, we explored a dataset from a Dutch hospital. We compared different methods, considering different prediction moments (depending of the amount of initial hours of stay), and we evaluate the gain in earlier predicting the need for aftercare.

Originele taal-2	Engels
Titel	Process Mining Workshops
Subtitel	ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers
Redacteuren	Marco Montali, Arik Senderovich, Matthias Weidlich
Uitgeverij	Springer
Pagina's	366-377
Aantal pagina's	12
ISBN van elektronische versie	978-3-031-27815-0
ISBN van geprinte versie	978-3-031-27814-3
DOI's	https://doi.org/10.1007/978-3-031-27815-0_27
Status	Gepubliceerd - 26 mrt. 2023
Evenement	International Workshops on EDBA, ML4PM, RPM, PODS4H, SA4PM, PQMI, EduPM, and DQT-PM, held at the International Conference on Process Mining, ICPM 2022 - Bozen-Bolzano, Italië Duur: 23 okt. 2022 → 28 okt. 2022

Publicatie series

Naam	Lecture Notes in Business Information Processing (LNBIP)
Volume	468
ISSN van geprinte versie	1865-1348
ISSN van elektronische versie	1865-1356

Workshop

Workshop	International Workshops on EDBA, ML4PM, RPM, PODS4H, SA4PM, PQMI, EduPM, and DQT-PM, held at the International Conference on Process Mining, ICPM 2022
Land/Regio	Italië
Stad	Bozen-Bolzano
Periode	23/10/22 → 28/10/22

Toegang tot document

10.1007/978-3-031-27815-0_27

pdfDefinitieve gepubliceerde versie, 2,71 MBLicentie: CC BY

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Dubbeldam, A. L., Ketykó, I., de Carvalho, R. M., & Mannhardt, F. (2023). Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study. In M. Montali, A. Senderovich, & M. Weidlich (editors), Process Mining Workshops: ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers (blz. 366-377). (Lecture Notes in Business Information Processing (LNBIP); Vol. 468). Springer. https://doi.org/10.1007/978-3-031-27815-0_27

Dubbeldam, Annika L. ; Ketykó, István ; de Carvalho, Renata M. et al. / Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study. Process Mining Workshops: ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers. redacteur / Marco Montali ; Arik Senderovich ; Matthias Weidlich. Springer, 2023. blz. 366-377 (Lecture Notes in Business Information Processing (LNBIP)).

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title = "Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study",

abstract = "Patients, when in a hospital, will go through a personalized treatment scheduled for many different reasons and with various outcomes. Furthermore, some patients and/or treatments require aftercare. Identifying the need for aftercare is crucial for improving the process of the patient and hospital. A late identification results in a patient staying longer than needed, occupying a bed that otherwise could serve another patient. In this paper, we will investigate to what extent events from the first hours of stay can help in predicting the need for aftercare. For that, we explored a dataset from a Dutch hospital. We compared different methods, considering different prediction moments (depending of the amount of initial hours of stay), and we evaluate the gain in earlier predicting the need for aftercare.",

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Dubbeldam, AL, Ketykó, I, de Carvalho, RM & Mannhardt, F 2023, Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study. in M Montali, A Senderovich & M Weidlich (reds.), Process Mining Workshops: ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers. Lecture Notes in Business Information Processing (LNBIP), vol. 468, Springer, blz. 366-377, International Workshops on EDBA, ML4PM, RPM, PODS4H, SA4PM, PQMI, EduPM, and DQT-PM, held at the International Conference on Process Mining, ICPM 2022, Bozen-Bolzano, Italië, 23/10/22. https://doi.org/10.1007/978-3-031-27815-0_27

Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study. / Dubbeldam, Annika L. (Corresponderende auteur); Ketykó, István; de Carvalho, Renata M. et al.
Process Mining Workshops: ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers. reds. / Marco Montali; Arik Senderovich; Matthias Weidlich. Springer, 2023. blz. 366-377 (Lecture Notes in Business Information Processing (LNBIP); Vol. 468).

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/Congresprocedure › Conferentiebijdrage › Academic › peer review

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AU - de Carvalho, Renata M.

AU - Mannhardt, Felix

PY - 2023/3/26

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N2 - Patients, when in a hospital, will go through a personalized treatment scheduled for many different reasons and with various outcomes. Furthermore, some patients and/or treatments require aftercare. Identifying the need for aftercare is crucial for improving the process of the patient and hospital. A late identification results in a patient staying longer than needed, occupying a bed that otherwise could serve another patient. In this paper, we will investigate to what extent events from the first hours of stay can help in predicting the need for aftercare. For that, we explored a dataset from a Dutch hospital. We compared different methods, considering different prediction moments (depending of the amount of initial hours of stay), and we evaluate the gain in earlier predicting the need for aftercare.

AB - Patients, when in a hospital, will go through a personalized treatment scheduled for many different reasons and with various outcomes. Furthermore, some patients and/or treatments require aftercare. Identifying the need for aftercare is crucial for improving the process of the patient and hospital. A late identification results in a patient staying longer than needed, occupying a bed that otherwise could serve another patient. In this paper, we will investigate to what extent events from the first hours of stay can help in predicting the need for aftercare. For that, we explored a dataset from a Dutch hospital. We compared different methods, considering different prediction moments (depending of the amount of initial hours of stay), and we evaluate the gain in earlier predicting the need for aftercare.

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Dubbeldam AL, Ketykó I, de Carvalho RM, Mannhardt F. Early Predicting the Need for Aftercare Based on Patients Events from the First Hours of Stay – A Case Study. In Montali M, Senderovich A, Weidlich M, redacteurs, Process Mining Workshops: ICPM 2022 International Workshops, Bozen-Bolzano, Italy, October 23–28, 2022, Revised Selected Papers. Springer. 2023. blz. 366-377. (Lecture Notes in Business Information Processing (LNBIP)). doi: 10.1007/978-3-031-27815-0_27