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. 2020 Nov 17;8(11):e21604. doi: 10.2196/21604

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©Daowei Li, Qiang Zhang, Yue Tan, Xinghuo Feng, Yuanyi Yue, Yuhan Bai, Jimeng Li, Jiahang Li, Youjun Xu, Shiyu Chen, Si-Yu Xiao, Muyan Sun, Xiaona Li, Fang Zhu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 17.11.2020.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

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Prediction based on computed tomography (CT) and laboratory features. (A) Spearman correlation heatmap between CT and laboratory features. “Peak” stands for peak location, and “height” stands for peak height. A summary table describing all CT and laboratory features and their abbreviations is provided in Multimedia Appendix 2. (B) Model accuracy metrics with an increased number of features. (C) Area under receiver operating characteristic of classification using a signature of 15 CT and laboratory features.