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. 2020 Jun 14;70(3):499–510. doi: 10.1136/gutjnl-2020-321106

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© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

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Single and dual time-point machine learning classification of disease activity for patients with Crohn’s disease (CD) and ulcerative colitis (UC) separately and combined cohorts based on proportional normalised operational taxonomic units (OTUs) present in >5% of samples. The receiver operating characteristics (ROC) curves for the boosted tree models on the second row were generated based on the ratio of each OTU between two consecutive time points. Only subjects that did not transition between disease states were included, Canadian and Irish combined. Below each ROC curve are variable importance displayed indicating the relative importance for the 10 OTUs with the highest gain for each model.