research-article

Modelling the hospital length of stay for patients undergoing laparoscopic appendectomy through a Multiple Regression Model

Authors:

Teresa Angela Trunfio,

Cristiana Giglio,

Giovanni Rossi,

Paolo Gargiulo,

Maria RomanoAuthors Info & Claims

BECB 2021: 2021 International Symposium on Biomedical Engineering and Computational Biology

Article No.: 36, Pages 1 - 5

https://doi.org/10.1145/3502060.3503644

Published: 14 February 2022 Publication History

Abstract

Healthcare facilities are under constant pressure to contain costs. This goal is becoming increasingly difficult to achieve due to the rapid growth of the complexity of the services and stringent quality requirements. Therefore, several strategies are implemented that make it possible to evaluate and obtain health processes as close as possible to standards. A widely used parameter in the literature is the length of stay (LOS). A patient's LOS can be affected by a number of factors, including their particular condition, medical history, or medical needs. Being able to know this variation a priori can be very important for the management of hospital resources, such as beds. In this study, a predictive model was built for the total LOS of patients undergoing laparoscopic appendectomy, one of the most common emergency procedures. The model was obtained using multiple linear regression with an R2 value of 0.638.

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Digital Library

Cited By

D'Amore AD'Onofrio GFidecicchi ATriassi MMarino M(2023)Predicting Post-Operative Length of Stay after Robotic Urologic Surgery from Hospital Stay Characteristics: A Monocentric Study2023 the 7th International Conference on Medical and Health Informatics (ICMHI)10.1145/3608298.3608333(188-191)Online publication date: 18-Oct-2023
https://doi.org/10.1145/3608298.3608333
Montella EMarino MLongo GTrama UFrangiosa ANardo ATriassi MImprota G(2023)Implementation of a regression model to study the hospital stay of patients undergoing Laparoscopic Appendectomy: a multicenter studyProceedings of the 2023 7th International Conference on Medical and Health Informatics10.1145/3608298.3608331(176-181)Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1145/3608298.3608331
Trunfio TScala AGiglio CRossi GBorrelli ARomano MImprota G(2022)Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomyBMC Medical Informatics and Decision Making10.1186/s12911-022-01884-922:1Online publication date: 24-May-2022
https://doi.org/10.1186/s12911-022-01884-9

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BECB 2021: 2021 International Symposium on Biomedical Engineering and Computational Biology

August 2021

262 pages

ISBN:9781450384117

DOI:10.1145/3502060

Copyright © 2021 ACM.

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Published: 14 February 2022

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BECB 2021: 2021 International Symposium on Biomedical Engineering and Computational Biology

August 13 - 15, 2021

Nanchang, China

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Cited By

D'Amore AD'Onofrio GFidecicchi ATriassi MMarino M(2023)Predicting Post-Operative Length of Stay after Robotic Urologic Surgery from Hospital Stay Characteristics: A Monocentric Study2023 the 7th International Conference on Medical and Health Informatics (ICMHI)10.1145/3608298.3608333(188-191)Online publication date: 18-Oct-2023
https://doi.org/10.1145/3608298.3608333
Montella EMarino MLongo GTrama UFrangiosa ANardo ATriassi MImprota G(2023)Implementation of a regression model to study the hospital stay of patients undergoing Laparoscopic Appendectomy: a multicenter studyProceedings of the 2023 7th International Conference on Medical and Health Informatics10.1145/3608298.3608331(176-181)Online publication date: 12-May-2023
https://dl.acm.org/doi/10.1145/3608298.3608331
Trunfio TScala AGiglio CRossi GBorrelli ARomano MImprota G(2022)Multiple regression model to analyze the total LOS for patients undergoing laparoscopic appendectomyBMC Medical Informatics and Decision Making10.1186/s12911-022-01884-922:1Online publication date: 24-May-2022
https://doi.org/10.1186/s12911-022-01884-9

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