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Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data
Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242357/ https://www.ncbi.nlm.nih.gov/pubmed/32439879 http://dx.doi.org/10.1038/s41598-020-64643-8 |
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author | Widera, Paweł Welsing, Paco M. J. Ladel, Christoph Loughlin, John Lafeber, Floris P. F. J. Petit Dop, Florence Larkin, Jonathan Weinans, Harrie Mobasheri, Ali Bacardit, Jaume |
author_facet | Widera, Paweł Welsing, Paco M. J. Ladel, Christoph Loughlin, John Lafeber, Floris P. F. J. Petit Dop, Florence Larkin, Jonathan Weinans, Harrie Mobasheri, Ali Bacardit, Jaume |
author_sort | Widera, Paweł |
collection | PubMed |
description | Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20–25% the number of patients who show no progression. This result might lead to more efficient clinical trials. |
format | Online Article Text |
id | pubmed-7242357 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-72423572020-05-29 Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data Widera, Paweł Welsing, Paco M. J. Ladel, Christoph Loughlin, John Lafeber, Floris P. F. J. Petit Dop, Florence Larkin, Jonathan Weinans, Harrie Mobasheri, Ali Bacardit, Jaume Sci Rep Article Conventional inclusion criteria used in osteoarthritis clinical trials are not very effective in selecting patients who would benefit from a therapy being tested. Typically majority of selected patients show no or limited disease progression during a trial period. As a consequence, the effect of the tested treatment cannot be observed, and the efforts and resources invested in running the trial are not rewarded. This could be avoided, if selection criteria were more predictive of the future disease progression. In this article, we formulated the patient selection problem as a multi-class classification task, with classes based on clinically relevant measures of progression (over a time scale typical for clinical trials). Using data from two long-term knee osteoarthritis studies OAI and CHECK, we tested multiple algorithms and learning process configurations (including multi-classifier approaches, cost-sensitive learning, and feature selection), to identify the best performing machine learning models. We examined the behaviour of the best models, with respect to prediction errors and the impact of used features, to confirm their clinical relevance. We found that the model-based selection outperforms the conventional inclusion criteria, reducing by 20–25% the number of patients who show no progression. This result might lead to more efficient clinical trials. Nature Publishing Group UK 2020-05-21 /pmc/articles/PMC7242357/ /pubmed/32439879 http://dx.doi.org/10.1038/s41598-020-64643-8 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Article Widera, Paweł Welsing, Paco M. J. Ladel, Christoph Loughlin, John Lafeber, Floris P. F. J. Petit Dop, Florence Larkin, Jonathan Weinans, Harrie Mobasheri, Ali Bacardit, Jaume Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data |
title | Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data |
title_full | Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data |
title_fullStr | Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data |
title_full_unstemmed | Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data |
title_short | Multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data |
title_sort | multi-classifier prediction of knee osteoarthritis progression from incomplete imbalanced longitudinal data |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7242357/ https://www.ncbi.nlm.nih.gov/pubmed/32439879 http://dx.doi.org/10.1038/s41598-020-64643-8 |
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