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. 2023 Dec 22;271(3):1133–1149. doi: 10.1007/s00415-023-12132-z

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© The Author(s) 2023

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/.

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Fig. 3 — Performance of random forest algorithm for predicting NEDA and change on therapy during follow-up. The figure shows the ROC curves on the left and the feature importance ranking (top 10) for the best predictor (Table 2) on the right. A staying on NEDA after 2 years follow-up; B starting DMDs during follow-up; C change from the first line to high-efficacy DMDs during follow-up