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2023 | OriginalPaper | Buchkapitel

Regression and Machine Learning Algorithm to Study the LOS of Patients Undergoing Hip Surgery

verfasst von : Arianna Scala, Marta Rosaria Marino, Massimo Majolo, Giuseppe Russo, Francesco Smeraglia, Morena Anna Basso, Francesco Bruno, Anna Borrelli, Alfonso Maria Ponsiglione

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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Abstract

Today, fracture surgery is a key part of a hospital’s orthopaedic department and usually involves significant clinical cost implications. The evaluation of hospitalization time for subjects suffering of hip fracture assumes a key role in the last years because it can affect the postoperative course and recovery of the patient. Length of stay (LOS) is a useful tool for monitoring patients and useful for hospital administrators to assess the efficiency of the hospital. Our aim is to investigate the LOS prediction for all patients with hip fracture hospitalized in two hospitals located in Campania Region, also comparing the obtained results. Different machine learning models and data analysis methodologies have been applied on a cohort of patients hospitalized in two different hospitals, also evaluating them in terms of accuracy and error.

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Vorheriges Kapitel Multiple Linear Regression to Analyze the Effect of Emergency Diagnostic Procedures on the Hospitalization

Nächstes Kapitel Regression Model to Predict LOS in General Medicine Department: A Bicentric Study

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Titel: Regression and Machine Learning Algorithm to Study the LOS of Patients Undergoing Hip Surgery
verfasst von: Arianna Scala
Marta Rosaria Marino
Massimo Majolo
Giuseppe Russo
Francesco Smeraglia
Morena Anna Basso
Francesco Bruno
Anna Borrelli
Alfonso Maria Ponsiglione
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_55

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