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Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases
OBJECTIVE: Predict long-term disease worsening and the removal of biosimilar medication in patients with rheumatic diseases. METHODOLOGY: Observational, retrospective descriptive study. Review of a database of patients with immune-mediated inflammatory rheumatic diseases who switched from a biologic...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688843/ https://www.ncbi.nlm.nih.gov/pubmed/38033115 http://dx.doi.org/10.1371/journal.pone.0291891 |
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author | Castro Corredor, David Calvo Pascual, Luis Ángel |
author_facet | Castro Corredor, David Calvo Pascual, Luis Ángel |
author_sort | Castro Corredor, David |
collection | PubMed |
description | OBJECTIVE: Predict long-term disease worsening and the removal of biosimilar medication in patients with rheumatic diseases. METHODOLOGY: Observational, retrospective descriptive study. Review of a database of patients with immune-mediated inflammatory rheumatic diseases who switched from a biological drug (biosimilar or non-biosimilar) to a biosimilar drug for at least 6 months. We selected the most important variables, from 18 variables, using mutual information tests. As patients with disease worsening are a minority, it is very difficult to make models with conventional machine learning techniques, where the best models would always be trivial. For this reason, we computed different types of imbalanced machine learning models, choosing those with better f1-score and mean ROC AUC. RESULTS: We computed the best-imbalanced machine learning models to predict disease worsening and the removal of the biosimilar, with f1-scores of 0.52 and 0.63, respectively. Both models are decision trees. In the first one, two important factors are switching of biosimilar and age, and in the second, the relevant variables are optimization and the value of the initial PCR. CONCLUSIONS: Biosimilar drugs do not always work well for rheumatic diseases. We obtain two imbalanced machine learning models to detect those cases, where the drug should be removed or where the activity of the disease increases from low to high. In our decision trees appear not previously studied variables, such as age, switching, or optimization. |
format | Online Article Text |
id | pubmed-10688843 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-106888432023-12-01 Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases Castro Corredor, David Calvo Pascual, Luis Ángel PLoS One Research Article OBJECTIVE: Predict long-term disease worsening and the removal of biosimilar medication in patients with rheumatic diseases. METHODOLOGY: Observational, retrospective descriptive study. Review of a database of patients with immune-mediated inflammatory rheumatic diseases who switched from a biological drug (biosimilar or non-biosimilar) to a biosimilar drug for at least 6 months. We selected the most important variables, from 18 variables, using mutual information tests. As patients with disease worsening are a minority, it is very difficult to make models with conventional machine learning techniques, where the best models would always be trivial. For this reason, we computed different types of imbalanced machine learning models, choosing those with better f1-score and mean ROC AUC. RESULTS: We computed the best-imbalanced machine learning models to predict disease worsening and the removal of the biosimilar, with f1-scores of 0.52 and 0.63, respectively. Both models are decision trees. In the first one, two important factors are switching of biosimilar and age, and in the second, the relevant variables are optimization and the value of the initial PCR. CONCLUSIONS: Biosimilar drugs do not always work well for rheumatic diseases. We obtain two imbalanced machine learning models to detect those cases, where the drug should be removed or where the activity of the disease increases from low to high. In our decision trees appear not previously studied variables, such as age, switching, or optimization. Public Library of Science 2023-11-30 /pmc/articles/PMC10688843/ /pubmed/38033115 http://dx.doi.org/10.1371/journal.pone.0291891 Text en © 2023 Castro Corredor, Calvo Pascual https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Castro Corredor, David Calvo Pascual, Luis Ángel Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases |
title | Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases |
title_full | Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases |
title_fullStr | Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases |
title_full_unstemmed | Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases |
title_short | Imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases |
title_sort | imbalanced machine learning classification models for removal biosimilar drugs and increased activity in patients with rheumatic diseases |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10688843/ https://www.ncbi.nlm.nih.gov/pubmed/38033115 http://dx.doi.org/10.1371/journal.pone.0291891 |
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