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Application of Machine Learning Models in Systemic Lupus Erythematosus
Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, th...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003088/ https://www.ncbi.nlm.nih.gov/pubmed/36901945 http://dx.doi.org/10.3390/ijms24054514 |
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author | Ceccarelli, Fulvia Natalucci, Francesco Picciariello, Licia Ciancarella, Claudia Dolcini, Giulio Gattamelata, Angelica Alessandri, Cristiano Conti, Fabrizio |
author_facet | Ceccarelli, Fulvia Natalucci, Francesco Picciariello, Licia Ciancarella, Claudia Dolcini, Giulio Gattamelata, Angelica Alessandri, Cristiano Conti, Fabrizio |
author_sort | Ceccarelli, Fulvia |
collection | PubMed |
description | Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario. |
format | Online Article Text |
id | pubmed-10003088 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-100030882023-03-11 Application of Machine Learning Models in Systemic Lupus Erythematosus Ceccarelli, Fulvia Natalucci, Francesco Picciariello, Licia Ciancarella, Claudia Dolcini, Giulio Gattamelata, Angelica Alessandri, Cristiano Conti, Fabrizio Int J Mol Sci Review Systemic Lupus Erythematosus (SLE) is a systemic autoimmune disease and is extremely heterogeneous in terms of immunological features and clinical manifestations. This complexity could result in a delay in the diagnosis and treatment introduction, with impacts on long-term outcomes. In this view, the application of innovative tools, such as machine learning models (MLMs), could be useful. Thus, the purpose of the present review is to provide the reader with information about the possible application of artificial intelligence in SLE patients from a medical perspective. To summarize, several studies have applied MLMs in large cohorts in different disease-related fields. In particular, the majority of studies focused on diagnosis and pathogenesis, disease-related manifestations, in particular Lupus Nephritis, outcomes and treatment. Nonetheless, some studies focused on peculiar features, such as pregnancy and quality of life. The review of published data demonstrated the proposal of several models with good performance, suggesting the possible application of MLMs in the SLE scenario. MDPI 2023-02-24 /pmc/articles/PMC10003088/ /pubmed/36901945 http://dx.doi.org/10.3390/ijms24054514 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 | Review Ceccarelli, Fulvia Natalucci, Francesco Picciariello, Licia Ciancarella, Claudia Dolcini, Giulio Gattamelata, Angelica Alessandri, Cristiano Conti, Fabrizio Application of Machine Learning Models in Systemic Lupus Erythematosus |
title | Application of Machine Learning Models in Systemic Lupus Erythematosus |
title_full | Application of Machine Learning Models in Systemic Lupus Erythematosus |
title_fullStr | Application of Machine Learning Models in Systemic Lupus Erythematosus |
title_full_unstemmed | Application of Machine Learning Models in Systemic Lupus Erythematosus |
title_short | Application of Machine Learning Models in Systemic Lupus Erythematosus |
title_sort | application of machine learning models in systemic lupus erythematosus |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10003088/ https://www.ncbi.nlm.nih.gov/pubmed/36901945 http://dx.doi.org/10.3390/ijms24054514 |
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