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Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics
BACKGROUND: We developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI). METHODS: We gathered the follow-up data of...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259419/ https://www.ncbi.nlm.nih.gov/pubmed/34229736 http://dx.doi.org/10.1186/s13075-021-02567-y |
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author | Koo, Bon San Eun, Seongho Shin, Kichul Yoon, Hyemin Hong, Chaelin Kim, Do-Hoon Hong, Seokchan Kim, Yong-Gil Lee, Chang-Keun Yoo, Bin Oh, Ji Seon |
author_facet | Koo, Bon San Eun, Seongho Shin, Kichul Yoon, Hyemin Hong, Chaelin Kim, Do-Hoon Hong, Seokchan Kim, Yong-Gil Lee, Chang-Keun Yoo, Bin Oh, Ji Seon |
author_sort | Koo, Bon San |
collection | PubMed |
description | BACKGROUND: We developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI). METHODS: We gathered the follow-up data of 1204 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. Remission was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost) were used for the predictions. The Shapley additive explanation (SHAP) value was used for interpretability of the predictions. RESULTS: The ranges for accuracy and area under the receiver operating characteristic of the newly developed machine learning model for predicting remission were 52.8–72.9% and 0.511–0.694, respectively. The Shapley plot in XAI showed that the impacts of the variables on predicting remission differed for each bDMARD. The most important features were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab, with mean SHAP values of − 0.250, − 0.234, − 0.514, − 0.227, − 0.804, and 0.135, respectively. CONCLUSIONS: Our proposed machine learning model successfully identified clinical features that were predictive of remission in each of the bDMARDs. This approach may be useful for improving treatment outcomes by identifying clinical information related to remissions in patients with rheumatoid arthritis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-021-02567-y. |
format | Online Article Text |
id | pubmed-8259419 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-82594192021-07-07 Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics Koo, Bon San Eun, Seongho Shin, Kichul Yoon, Hyemin Hong, Chaelin Kim, Do-Hoon Hong, Seokchan Kim, Yong-Gil Lee, Chang-Keun Yoo, Bin Oh, Ji Seon Arthritis Res Ther Research Article BACKGROUND: We developed a model to predict remissions in patients treated with biologic disease-modifying anti-rheumatic drugs (bDMARDs) and to identify important clinical features associated with remission using explainable artificial intelligence (XAI). METHODS: We gathered the follow-up data of 1204 patients treated with bDMARDs (etanercept, adalimumab, golimumab, infliximab, abatacept, and tocilizumab) from the Korean College of Rheumatology Biologics and Targeted Therapy Registry. Remission was predicted at 1-year follow-up using baseline clinical data obtained at the time of enrollment. Machine learning methods (e.g., lasso, ridge, support vector machine, random forest, and XGBoost) were used for the predictions. The Shapley additive explanation (SHAP) value was used for interpretability of the predictions. RESULTS: The ranges for accuracy and area under the receiver operating characteristic of the newly developed machine learning model for predicting remission were 52.8–72.9% and 0.511–0.694, respectively. The Shapley plot in XAI showed that the impacts of the variables on predicting remission differed for each bDMARD. The most important features were age for adalimumab, rheumatoid factor for etanercept, erythrocyte sedimentation rate for infliximab and golimumab, disease duration for abatacept, and C-reactive protein for tocilizumab, with mean SHAP values of − 0.250, − 0.234, − 0.514, − 0.227, − 0.804, and 0.135, respectively. CONCLUSIONS: Our proposed machine learning model successfully identified clinical features that were predictive of remission in each of the bDMARDs. This approach may be useful for improving treatment outcomes by identifying clinical information related to remissions in patients with rheumatoid arthritis. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13075-021-02567-y. BioMed Central 2021-07-06 2021 /pmc/articles/PMC8259419/ /pubmed/34229736 http://dx.doi.org/10.1186/s13075-021-02567-y Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis 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/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Article Koo, Bon San Eun, Seongho Shin, Kichul Yoon, Hyemin Hong, Chaelin Kim, Do-Hoon Hong, Seokchan Kim, Yong-Gil Lee, Chang-Keun Yoo, Bin Oh, Ji Seon Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics |
title | Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics |
title_full | Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics |
title_fullStr | Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics |
title_full_unstemmed | Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics |
title_short | Machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics |
title_sort | machine learning model for identifying important clinical features for predicting remission in patients with rheumatoid arthritis treated with biologics |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8259419/ https://www.ncbi.nlm.nih.gov/pubmed/34229736 http://dx.doi.org/10.1186/s13075-021-02567-y |
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