@article{SUN2024, 
author = {Yizhu SUN and Yujie LI and Hao LIANG and Xiang LIU and Jiahao HUANG and Xin SHU and Ailin SONG and Zhiyong YANG and Bin YI},
title = {Establishment of risk prediction model for postoperative liver injury after non-liver surgery based on different machine learning algorithms},
year = {2024},
journal = {Journal of Army Medical University},
volume = {46},
number = {7},
pages = {760-767},
keywords = {machine learning, predicting model, postoperative liver injury},
url = {https://www.sciopen.com/article/10.16016/j.2097-0927.202312018},
doi = {10.16016/j.2097-0927.202312018},
abstract = {ObjectiveTo construct a machine learning prediction model for postoperative liver injury in patients with non-liver surgery based on preoperative and intraoperative medication indicators.MethodsA case-control study was conducted on 315 patients with liver injury after non-liver surgery selected from the databases developed by 3 large general hospitals from January 2014 to September 2022. With the positive/negative ratio of 1:3, 928 cases in corresponding period with non-liver surgery and without liver injury were randomly matched as negative control cases. These 1 243 patients were randomly divided into the modeling group (n=869) and the validation group (n=374) in a ratio of 7:3 using the R language setting code. Preoperative clinical indicators (basic information, medical history, relevant scale score, surgical information and results of laboratory tests) and intraoperative medication were used to construct the prediction model for liver injury after non-liver surgery based on 4 machine learning algorithms, k-nearest neighbor (KNN), support vector machine linear (SVM), logic regression (LR) and extreme gradient boosting (XGBoost). In the validation group, receiver operating characteristic (ROC) curve, precision-recall curve (P-R), decision curve analysis (DCA) curve, Kappa value, sensitivity, specificity, Brier score, and F1 score were applied to evaluate the efficacy of model.ResultsThe model established by 4 machine learning algorithms to predict postoperative liver injury after non-liver surgery was optimal using the XGBoost algorithm. The area under the receiver operating characteristic curve (AUROC) was 0.916 (95%CI: 0.883~0.949), area under the precision-recall curve (AUPRC) was 0.841, Brier score was 0.097, and sensitivity and specificity was 78.95% and 87.10%, respectively.ConclusionThe postoperative liver injury prediction model for non-liver surgery based on the XGBoost algorithm has effective prediction for the occurrence of postoperative liver injury.}
}