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Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test
Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068780/ https://www.ncbi.nlm.nih.gov/pubmed/35508670 http://dx.doi.org/10.1038/s41598-022-11361-y |
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author | Matsuo, Hidemasa Kamada, Mayumi Imamura, Akari Shimizu, Madoka Inagaki, Maiko Tsuji, Yuko Hashimoto, Motomu Tanaka, Masao Ito, Hiromu Fujii, Yasutomo |
author_facet | Matsuo, Hidemasa Kamada, Mayumi Imamura, Akari Shimizu, Madoka Inagaki, Maiko Tsuji, Yuko Hashimoto, Motomu Tanaka, Masao Ito, Hiromu Fujii, Yasutomo |
author_sort | Matsuo, Hidemasa |
collection | PubMed |
description | Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options. |
format | Online Article Text |
id | pubmed-9068780 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90687802022-05-05 Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test Matsuo, Hidemasa Kamada, Mayumi Imamura, Akari Shimizu, Madoka Inagaki, Maiko Tsuji, Yuko Hashimoto, Motomu Tanaka, Masao Ito, Hiromu Fujii, Yasutomo Sci Rep Article Recent effective therapies enable most rheumatoid arthritis (RA) patients to achieve remission; however, some patients experience relapse. We aimed to predict relapse in RA patients through machine learning (ML) using data on ultrasound (US) examination and blood test. Overall, 210 patients with RA in remission at baseline were dichotomized into remission (n = 150) and relapse (n = 60) based on the disease activity at 2-year follow-up. Three ML classifiers [Logistic Regression, Random Forest, and extreme gradient boosting (XGBoost)] and data on 73 features (14 US examination data, 54 blood test data, and five data on patient information) at baseline were used for predicting relapse. The best performance was obtained using the XGBoost classifier (area under the receiver operator characteristic curve (AUC) = 0.747), compared with Random Forest and Logistic Regression (AUC = 0.719 and 0.701, respectively). In the XGBoost classifier prediction, ten important features, including wrist/metatarsophalangeal superb microvascular imaging scores, were selected using the recursive feature elimination method. The performance was superior to that predicted by researcher-selected features, which are conventional prognostic markers. These results suggest that ML can provide an accurate prediction of relapse in RA patients, and the use of predictive algorithms may facilitate personalized treatment options. Nature Publishing Group UK 2022-05-04 /pmc/articles/PMC9068780/ /pubmed/35508670 http://dx.doi.org/10.1038/s41598-022-11361-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Matsuo, Hidemasa Kamada, Mayumi Imamura, Akari Shimizu, Madoka Inagaki, Maiko Tsuji, Yuko Hashimoto, Motomu Tanaka, Masao Ito, Hiromu Fujii, Yasutomo Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test |
title | Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test |
title_full | Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test |
title_fullStr | Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test |
title_full_unstemmed | Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test |
title_short | Machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test |
title_sort | machine learning-based prediction of relapse in rheumatoid arthritis patients using data on ultrasound examination and blood test |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9068780/ https://www.ncbi.nlm.nih.gov/pubmed/35508670 http://dx.doi.org/10.1038/s41598-022-11361-y |
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