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Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach

Our paper proposes the first machine learning model to predict long-term mortality in patients with diabetic foot ulcers (DFUs). The study includes 635 patients with DFUs admitted from January 2007 to December 2017, with a follow-up period extending until December 2020. Two multilayer perceptron (ML...

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Autores principales: Popa, Alina Delia, Gavril, Radu Sebastian, Popa, Iolanda Valentina, Mihalache, Laura, Gherasim, Andreea, Niță, George, Graur, Mariana, Arhire, Lidia Iuliana, Niță, Otilia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10531505/
https://www.ncbi.nlm.nih.gov/pubmed/37762756
http://dx.doi.org/10.3390/jcm12185816
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author Popa, Alina Delia
Gavril, Radu Sebastian
Popa, Iolanda Valentina
Mihalache, Laura
Gherasim, Andreea
Niță, George
Graur, Mariana
Arhire, Lidia Iuliana
Niță, Otilia
author_facet Popa, Alina Delia
Gavril, Radu Sebastian
Popa, Iolanda Valentina
Mihalache, Laura
Gherasim, Andreea
Niță, George
Graur, Mariana
Arhire, Lidia Iuliana
Niță, Otilia
author_sort Popa, Alina Delia
collection PubMed
description Our paper proposes the first machine learning model to predict long-term mortality in patients with diabetic foot ulcers (DFUs). The study includes 635 patients with DFUs admitted from January 2007 to December 2017, with a follow-up period extending until December 2020. Two multilayer perceptron (MLP) classifiers were developed. The first MLP model was developed to predict whether the patient will die in the next 5 years after the current hospitalization. The second MLP classifier was built to estimate whether the patient will die in the following 10 years. The 5-year and 10-year mortality models were based on the following predictors: age; the University of Texas Staging System for Diabetic Foot Ulcers score; the Wagner–Meggitt classification; the Saint Elian Wound Score System; glomerular filtration rate; topographic aspects and the depth of the lesion; and the presence of foot ischemia, cardiovascular disease, diabetic nephropathy, and hypertension. The accuracy for the 5-year and 10-year models was 0.7717 and 0.7598, respectively (for the training set) and 0.7244 and 0.7087, respectively (for the test set). Our findings indicate that it is possible to predict with good accuracy the risk of death in patients with DFUs using non-invasive and low-cost predictors.
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spelling pubmed-105315052023-09-28 Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach Popa, Alina Delia Gavril, Radu Sebastian Popa, Iolanda Valentina Mihalache, Laura Gherasim, Andreea Niță, George Graur, Mariana Arhire, Lidia Iuliana Niță, Otilia J Clin Med Article Our paper proposes the first machine learning model to predict long-term mortality in patients with diabetic foot ulcers (DFUs). The study includes 635 patients with DFUs admitted from January 2007 to December 2017, with a follow-up period extending until December 2020. Two multilayer perceptron (MLP) classifiers were developed. The first MLP model was developed to predict whether the patient will die in the next 5 years after the current hospitalization. The second MLP classifier was built to estimate whether the patient will die in the following 10 years. The 5-year and 10-year mortality models were based on the following predictors: age; the University of Texas Staging System for Diabetic Foot Ulcers score; the Wagner–Meggitt classification; the Saint Elian Wound Score System; glomerular filtration rate; topographic aspects and the depth of the lesion; and the presence of foot ischemia, cardiovascular disease, diabetic nephropathy, and hypertension. The accuracy for the 5-year and 10-year models was 0.7717 and 0.7598, respectively (for the training set) and 0.7244 and 0.7087, respectively (for the test set). Our findings indicate that it is possible to predict with good accuracy the risk of death in patients with DFUs using non-invasive and low-cost predictors. MDPI 2023-09-07 /pmc/articles/PMC10531505/ /pubmed/37762756 http://dx.doi.org/10.3390/jcm12185816 Text en © 2023 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
Popa, Alina Delia
Gavril, Radu Sebastian
Popa, Iolanda Valentina
Mihalache, Laura
Gherasim, Andreea
Niță, George
Graur, Mariana
Arhire, Lidia Iuliana
Niță, Otilia
Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach
title Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach
title_full Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach
title_fullStr Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach
title_full_unstemmed Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach
title_short Survival Prediction in Diabetic Foot Ulcers: A Machine Learning Approach
title_sort survival prediction in diabetic foot ulcers: a machine learning approach
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10531505/
https://www.ncbi.nlm.nih.gov/pubmed/37762756
http://dx.doi.org/10.3390/jcm12185816
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