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. 2022 Nov 2;12:986867. doi: 10.3389/fonc.2022.986867

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Copyright © 2022 Wang, Zheng, Liao, Geng, Li, Zhang, Shang, Liu, Yu, Huang, Liu, Liu, Liu, Wang, Liu, Miao, Li, Zhang, Huang, Zhang, Qi and Chen

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Summary Shapley additive explanations plot revealing the impact of individual clinical variables. (A) The importance matrix plot of clinical variables is derived using the LightGBM model. The matrix plot ranks the importance of the variables selected for the final analysis, revealing the contribution of each variable to PHLF versus non-PHLF. (B) The SHAP summary plot of the LightGBM model is shown. The higher the SHAP value for each clinical variable, the higher risk of PHLF. LightGBM, light gradient boosting machines; PHLF, post-hepatectomy liver failure.