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Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture

Background: The number of patients with fragility fracture has been increasing. Although the increasing number of patients with fragility fracture increased the rate of fracture (refracture), the causes of refracture are multifactorial, and its predictors are still not clarified. In this issue, we c...

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Autores principales: Shimizu, Hirokazu, Enda, Ken, Shimizu, Tomohiro, Ishida, Yusuke, Ishizu, Hotaka, Ise, Koki, Tanaka, Shinya, Iwasaki, Norimasa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999234/
https://www.ncbi.nlm.nih.gov/pubmed/35407629
http://dx.doi.org/10.3390/jcm11072021
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author Shimizu, Hirokazu
Enda, Ken
Shimizu, Tomohiro
Ishida, Yusuke
Ishizu, Hotaka
Ise, Koki
Tanaka, Shinya
Iwasaki, Norimasa
author_facet Shimizu, Hirokazu
Enda, Ken
Shimizu, Tomohiro
Ishida, Yusuke
Ishizu, Hotaka
Ise, Koki
Tanaka, Shinya
Iwasaki, Norimasa
author_sort Shimizu, Hirokazu
collection PubMed
description Background: The number of patients with fragility fracture has been increasing. Although the increasing number of patients with fragility fracture increased the rate of fracture (refracture), the causes of refracture are multifactorial, and its predictors are still not clarified. In this issue, we collected a registry-based longitudinal dataset that contained more than 7000 patients with fragility fractures treated surgically to detect potential predictors for clinical refracture. Methods: Based on the fact that machine learning algorithms are often used for the analysis of a large-scale dataset, we developed automatic prediction models and clarified the relevant features for patients with clinical refracture. Formats of input data containing perioperative clinical information were table data. Clinical refracture was documented as the primary outcome if the diagnosis of fracture was made at postoperative outpatient care. A decision-tree-based model, LightGBM, had moderate accuracy for the prediction in the test and the independent dataset, whereas the other models had poor accuracy or worse. Results: From a clinical perspective, rheumatoid arthritis (RA) and chronic kidney disease (CKD) were noted as the relevant features for patients with clinical refracture, both of which were associated with secondary osteoporosis. Conclusion: The decision-tree-based algorithm showed the precise prediction of clinical refracture, in which RA and CKD were detected as the potential predictors. Understanding these predictors may improve the management of patients with fragility fractures.
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spelling pubmed-89992342022-04-12 Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture Shimizu, Hirokazu Enda, Ken Shimizu, Tomohiro Ishida, Yusuke Ishizu, Hotaka Ise, Koki Tanaka, Shinya Iwasaki, Norimasa J Clin Med Article Background: The number of patients with fragility fracture has been increasing. Although the increasing number of patients with fragility fracture increased the rate of fracture (refracture), the causes of refracture are multifactorial, and its predictors are still not clarified. In this issue, we collected a registry-based longitudinal dataset that contained more than 7000 patients with fragility fractures treated surgically to detect potential predictors for clinical refracture. Methods: Based on the fact that machine learning algorithms are often used for the analysis of a large-scale dataset, we developed automatic prediction models and clarified the relevant features for patients with clinical refracture. Formats of input data containing perioperative clinical information were table data. Clinical refracture was documented as the primary outcome if the diagnosis of fracture was made at postoperative outpatient care. A decision-tree-based model, LightGBM, had moderate accuracy for the prediction in the test and the independent dataset, whereas the other models had poor accuracy or worse. Results: From a clinical perspective, rheumatoid arthritis (RA) and chronic kidney disease (CKD) were noted as the relevant features for patients with clinical refracture, both of which were associated with secondary osteoporosis. Conclusion: The decision-tree-based algorithm showed the precise prediction of clinical refracture, in which RA and CKD were detected as the potential predictors. Understanding these predictors may improve the management of patients with fragility fractures. MDPI 2022-04-05 /pmc/articles/PMC8999234/ /pubmed/35407629 http://dx.doi.org/10.3390/jcm11072021 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
Shimizu, Hirokazu
Enda, Ken
Shimizu, Tomohiro
Ishida, Yusuke
Ishizu, Hotaka
Ise, Koki
Tanaka, Shinya
Iwasaki, Norimasa
Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
title Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
title_full Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
title_fullStr Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
title_full_unstemmed Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
title_short Machine Learning Algorithms: Prediction and Feature Selection for Clinical Refracture after Surgically Treated Fragility Fracture
title_sort machine learning algorithms: prediction and feature selection for clinical refracture after surgically treated fragility fracture
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8999234/
https://www.ncbi.nlm.nih.gov/pubmed/35407629
http://dx.doi.org/10.3390/jcm11072021
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