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2023 | OriginalPaper | Buchkapitel

Multiple Regression Model to Analyze the Length of Stay for Patients Undergoing Laparoscopic Appendectomy: A Bicentric Study

verfasst von : Emma Montella, Marta Rosaria Marino, Alessandro Frangiosa, Giuseppe Mazia, Massimo Majolo, Eliana Raiola, Giuseppe Russo, Giuseppe Longo, Giovanni Rossi, Anna Borrelli, Maria Triassi

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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Abstract

Cost-containment and efficiency are aspects that have more and more weight in the evaluation of the performance of healthcare facilities. This trend, coupled with the ever-rising complexity of the services and quality standards, has called for a great attention to the rationalization of resources. Our aim is to predict the Length Of Stay (LOS) by investigating several variabilities both intrinsic (i.e. age, comorbidities) and extrinsic (i.e. complications, pre-operative LOS) to the patient and have great impact on the economic expenditure. Therefore, healthcare facilities are in dire need of new tools to know a priori patient’s needs. This study has the purpose to design and compare different Artificial Intelligence (AI) models for predicting the subject’s LOS under appendectomy. In particular, the AI model has been designed in a previous work using data extracted from an Italian hospital, the University Hospital “San Giovanni di Dio e Ruggi d’Aragona” of Salerno through Multiple Linear Regression. In this paper the results were compared with a similar sample from the AORN “Antonio Cardarelli” of Napoli to evaluate its efficacy.

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Titel: Multiple Regression Model to Analyze the Length of Stay for Patients Undergoing Laparoscopic Appendectomy: A Bicentric Study
verfasst von: Emma Montella
Marta Rosaria Marino
Alessandro Frangiosa
Giuseppe Mazia
Massimo Majolo
Eliana Raiola
Giuseppe Russo
Giuseppe Longo
Giovanni Rossi
Anna Borrelli
Maria Triassi
Verlag: Springer International Publishing
Buch: Biomedical and Computational Biology
Print ISBN: 978-3-031-25190-0

Electronic ISBN: 978-3-031-25191-7

Copyright-Jahr: 2023
DOI: https://doi.org/10.1007/978-3-031-25191-7_37

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