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

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Book cover Biomedical and Computational Biology (BECB 2022)

Part of the book series: Lecture Notes in Computer Science ((LNBI,volume 13637))

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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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Authors and Affiliations

  1. Department of Public Health, University of Naples “Federico II”, Naples, Italy

    Emma Montella, Marta Rosaria Marino, Alessandro Frangiosa, Giuseppe Mazia & Maria Triassi

  2. A.O.R.N. Antonio Cardarelli, Naples, Italy

    Massimo Majolo, Eliana Raiola, Giuseppe Russo & Giuseppe Longo

  3. “San Giovanni di Dio e Ruggi d’Aragona” University Hospital, Salerno, Italy

    Giovanni Rossi & Anna Borrelli

  4. Interdepartmental Center for Research in Healthcare Management and Innovation in Healthcare (CIRMIS), University of Naples “Federico II”, Naples, Italy

    Maria Triassi

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  1. Emma Montella
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  2. Marta Rosaria Marino
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  3. Alessandro Frangiosa
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Correspondence to Marta Rosaria Marino .

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Editors and Affiliations

  1. University of Technology Sydney, Sydney, NSW, Australia

    Shiping Wen

  2. Nanchang Hangkong University, Nanchang, China

    Cihui Yang

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Montella, E. et al. (2023). Multiple Regression Model to Analyze the Length of Stay for Patients Undergoing Laparoscopic Appendectomy: A Bicentric Study. In: Wen, S., Yang, C. (eds) Biomedical and Computational Biology. BECB 2022. Lecture Notes in Computer Science(), vol 13637. Springer, Cham. https://doi.org/10.1007/978-3-031-25191-7_37

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_37

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