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. 2022 Apr 13;12:6193. doi: 10.1038/s41598-022-10136-9

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© The Author(s) 2022

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/.

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Testing performances of the developed models. The model using the deep imaging features performed worse on the testing set than on the validation set (0.70 compared to 0.88), pointing to some overfitting. In contrast, the mortality score model similarly (0.66 compared to 0.62). Combining the imaging features with the mortality score yielded a slightly higher AUC of 0.74, using the same SVM classifier. There are 145 positive samples (survival) and 39 negative samples (Deaths) in the testing set.