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2023 | OriginalPaper | Buchkapitel

Machine Learning Algorithms to Study Features Affecting the Length of Stay in Patients with Lower Limb Fractures: A Bicentric Study

verfasst von : Ida Santalucia, Marta Rosaria Marino, Massimo Majolo, Eliana Raiola, Giuseppe Russo, Giuseppe Longo, Morena Anna Basso, Giovanni Balato, Andrea Lombardi, Anna Borrelli, Maria Triassi

Erschienen in: Biomedical and Computational Biology

Verlag: Springer International Publishing

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Abstract

The estimation of the length of stay (LOS) can support the analysis of practitioners for improving the hospital efficiency. For the appropriate management of beds and to reduce costs, it is necessary to analyse and evaluate procedures for reducing hospital stays. For patients with orthopedic trauma, especially with lower limb fractures, LOS becomes an important parameter to estimate. The aim of this study is to study LOS for all patients with lower limb fractures in the University Hospital “San Giovanni di Dio and Ruggi d’Aragona” of Salerno and A.O.R.N. “Antonio Cardarelli” of Naples. The analysis was conducted by implementing different well-known Artificial Intelligence models, whose accuracy for the Hospital “San Giovanni di Dio and Ruggi d’Aragona” was 92,0% (NB algorithm) while for the A.O.R.N. “A. Cardarelli” the best performance was obtained with RF algorithm with an accuracy of 95,92%. These results obtained from the estimation of LOS show promise and therefore can be a valuable help in the management process of the hospital.

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Titel: Machine Learning Algorithms to Study Features Affecting the Length of Stay in Patients with Lower Limb Fractures: A Bicentric Study
verfasst von: Ida Santalucia
Marta Rosaria Marino
Massimo Majolo
Eliana Raiola
Giuseppe Russo
Giuseppe Longo
Morena Anna Basso
Giovanni Balato
Andrea Lombardi
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_43

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Abstract

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