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. 2019 Dec 19;6:328. doi: 10.1038/s41597-019-0337-6

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© The Author(s) 2019

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/.

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Fig. 4 — Framework and pipeline of work. Four different data sets were run through the pipeline created consisting of feature selection and machine learning performance evaluation for best predictor of multiorgan dysfunction. These four datasets had the same patients studied in a longitudinal fashion with assessment of the three different time points and their combination.