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. 2020 Nov 11;22(11):e23128. doi: 10.2196/23128

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©Pan Pan, Yichao Li, Yongjiu Xiao, Bingchao Han, Longxiang Su, Mingliang Su, Yansheng Li, Siqi Zhang, Dapeng Jiang, Xia Chen, Fuquan Zhou, Ling Ma, Pengtao Bao, Lixin Xie. Originally published in the Journal of Medical Internet Research (http://www.jmir.org), 11.11.2020.

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 the Journal of Medical Internet Research, is properly cited. The complete bibliographic information, a link to the original publication on http://www.jmir.org/, as well as this copyright and license information must be included.

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Interpretation of sample prediction results by randomly drawing samples to make model predictions and observing the model through the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. (A) The four different prediction behaviors of the model (true negative, true positive, false negative, and false positive); (B) the ratios of the four prediction behaviors of the model on the training set and the test set. A:LB: albumin level; CR: creatinine: EOS%: eosinophil percentage; LDH: lactate dehydrogenase; LYM%: lymphocyte percentage; NEUT%: neutrophil percentage; PT: prothrombin time; T-Bil: total bilirubin.