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Regression Models to Study Emergency Surgery Admissions

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Biomedical and Computational Biology (BECB 2022)

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

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Abstract

In the last few decades there has been considerable interest in facing the challenging problem of improving emergency general surgery management.

The most crowded wards is the emergency one, especially due to population aging which results in higher mortality rates and prolonged hospital stay (LOS) w.r.t. the elective surgery intervention. This is mostly due to the intrinsic stochastic nature of patience arrival, and the heterogeneity of the medical procedures required. In this context, our work aims at reducing the LOS by using predictive algorithms to improve the emergency department management. In particular, we examine the LOS variation for cholecystectomy interventions in the emergency general surgery through three different machine learning algorithms and the linear regression analysis, with the purpose of identifying the best prediction model as long as those factors that have the highest contribution in enhancing the LOS, in order to reduce it and improve both the subject satisfaction and the overall quality of the provided health services.

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Author information

Authors and Affiliations

  1. Department of Electrical Engineering and Information Technology, University of Study of Naples “Federico II”, Naples, Italy

    Martina Profeta & Alfonso Maria Ponsiglione

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

    Marta Rosaria Marino, Francesco Smeraglia, Enrico Festa & Maria Triassi

  3. University of Rome “La Sapienza”, Rome, Italy

    Cristiana Giglio

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

    Andrea Lombardi & Anna Borrelli

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

    Maria Triassi

Authors
  1. Martina Profeta
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  2. Marta Rosaria Marino
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  3. Cristiana Giglio
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  4. Francesco Smeraglia
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  5. Enrico Festa
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  6. Andrea Lombardi
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  7. Anna Borrelli
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  8. Maria Triassi
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  9. Alfonso Maria Ponsiglione
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Corresponding author

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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Profeta, M. et al. (2023). Regression Models to Study Emergency Surgery Admissions. 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_51

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  • DOI: https://doi.org/10.1007/978-3-031-25191-7_51

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