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Erschienen in:
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2023 | OriginalPaper | Buchkapitel

Regression Models to Study Emergency Surgery Admissions

verfasst von : Martina Profeta, Marta Rosaria Marino, Cristiana Giglio, Francesco Smeraglia, Enrico Festa, Andrea Lombardi, Anna Borrelli, Maria Triassi, Alfonso Maria Ponsiglione

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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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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Vorheriges Kapitel Analysis of Hospital Admissions of Neurological Patients in the COVID-19 Era: Comparison Between Hospitals
Nächstes Kapitel Impact of COVID-19 in a Surgery Department: Comparison Between Two Italian Hospitals
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Metadaten
Titel
Regression Models to Study Emergency Surgery Admissions
verfasst von
Martina Profeta
Marta Rosaria Marino
Cristiana Giglio
Francesco Smeraglia
Enrico Festa
Andrea Lombardi
Anna Borrelli
Maria Triassi
Alfonso Maria Ponsiglione
Copyright-Jahr
2023
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_51

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