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Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture
Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoper...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029792/ https://www.ncbi.nlm.nih.gov/pubmed/35447732 http://dx.doi.org/10.3390/bioengineering9040172 |
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author | Ricciardi, Carlo Ponsiglione, Alfonso Maria Scala, Arianna Borrelli, Anna Misasi, Mario Romano, Gaetano Russo, Giuseppe Triassi, Maria Improta, Giovanni |
author_facet | Ricciardi, Carlo Ponsiglione, Alfonso Maria Scala, Arianna Borrelli, Anna Misasi, Mario Romano, Gaetano Russo, Giuseppe Triassi, Maria Improta, Giovanni |
author_sort | Ricciardi, Carlo |
collection | PubMed |
description | Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R(2)) was achieved by the support vector machine (R(2) = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare. |
format | Online Article Text |
id | pubmed-9029792 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90297922022-04-23 Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture Ricciardi, Carlo Ponsiglione, Alfonso Maria Scala, Arianna Borrelli, Anna Misasi, Mario Romano, Gaetano Russo, Giuseppe Triassi, Maria Improta, Giovanni Bioengineering (Basel) Article Fractures of the femur are a frequent problem in elderly people, and it has been demonstrated that treating them with a diagnostic–therapeutic–assistance path within 48 h of admission to the hospital reduces complications and shortens the length of the hospital stay (LOS). In this paper, the preoperative data of 1082 patients were used to further extend the previous research and to generate several models that are capable of predicting the overall LOS: First, the LOS, measured in days, was predicted through a regression analysis; then, it was grouped by weeks and was predicted with a classification analysis. The KNIME analytics platform was applied to divide the dataset for a hold-out cross-validation, perform a multiple linear regression and implement machine learning algorithms. The best coefficient of determination (R(2)) was achieved by the support vector machine (R(2) = 0.617), while the mean absolute error was similar for all the algorithms, ranging between 2.00 and 2.11 days. With regard to the classification analysis, all the algorithms surpassed 80% accuracy, and the most accurate algorithm was the radial basis function network, at 83.5%. The use of these techniques could be a valuable support tool for doctors to better manage orthopaedic departments and all their resources, which would reduce both waste and costs in the context of healthcare. MDPI 2022-04-14 /pmc/articles/PMC9029792/ /pubmed/35447732 http://dx.doi.org/10.3390/bioengineering9040172 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Ricciardi, Carlo Ponsiglione, Alfonso Maria Scala, Arianna Borrelli, Anna Misasi, Mario Romano, Gaetano Russo, Giuseppe Triassi, Maria Improta, Giovanni Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
title | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
title_full | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
title_fullStr | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
title_full_unstemmed | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
title_short | Machine Learning and Regression Analysis to Model the Length of Hospital Stay in Patients with Femur Fracture |
title_sort | machine learning and regression analysis to model the length of hospital stay in patients with femur fracture |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9029792/ https://www.ncbi.nlm.nih.gov/pubmed/35447732 http://dx.doi.org/10.3390/bioengineering9040172 |
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