干旱区生态环境知识资源中心(Arid): An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury

Arid

DOI	10.1371/journal.pone.0303469
	An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury
	Wen, Chengli; Zhang, Xu; Li, Yong; Xiao, Wanmeng; Hu, Qinxue; Lei, Xianying; Xu, Tao; Liang, Sicheng; Gao, Xiaolan; Zhang, Chao; Yu, Zehui; Lue, Muhan
通讯作者	Lue, MH
来源期刊	PLOS ONE
ISSN	1932-6203
出版年	2024
卷号	19 期号:5
英文摘要	Sepsis-Associated Liver Injury (SALI) is an independent risk factor for death from sepsis. The aim of this study was to develop an interpretable machine learning model for early prediction of 28-day mortality in patients with SALI. Data from the Medical Information Mart for Intensive Care (MIMIC-IV, v2.2, MIMIC-III, v1.4) were used in this study. The study cohort from MIMIC-IV was randomized to the training set (0.7) and the internal validation set (0.3), with MIMIC-III (2001 to 2008) as external validation. The features with more than 20% missing values were deleted and the remaining features were multiple interpolated. Lasso-CV that lasso linear model with iterative fitting along a regularization path in which the best model is selected by cross-validation was used to select important features for model development. Eight machine learning models including Random Forest (RF), Logistic Regression, Decision Tree, Extreme Gradient Boost (XGBoost), K Nearest Neighbor, Support Vector Machine, Generalized Linear Models in which the best model is selected by cross-validation (CV_glmnet), and Linear Discriminant Analysis (LDA) were developed. Shapley additive interpretation (SHAP) was used to improve the interpretability of the optimal model. At last, a total of 1043 patients were included, of whom 710 were from MIMIC-IV and 333 from MIMIC-III. Twenty-four clinically relevant parameters were selected for model construction. For the prediction of 28-day mortality of SALI in the internal validation set, the area under the curve (AUC (95% CI)) of RF was 0.79 (95% CI: 0.73-0.86), and which performed the best. Compared with the traditional disease severity scores including Oxford Acute Severity of Illness Score (OASIS), Sequential Organ Failure Assessment (SOFA), Simplified Acute Physiology Score II (SAPS II), Logistic Organ Dysfunction Score (LODS), Systemic Inflammatory Response Syndrome (SIRS), and Acute Physiology Score III (APS III), RF also had the best performance. SHAP analysis found that Urine output, Charlson Comorbidity Index (CCI), minimal Glasgow Coma Scale (GCS_min), blood urea nitrogen (BUN) and admission_age were the five most important features affecting RF model. Therefore, RF has good predictive ability for 28-day mortality prediction in SALI. Urine output, CCI, GCS_min, BUN and age at admission(admission_age) within 24 h after intensive care unit(ICU) admission contribute significantly to model prediction.
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:001228144300019
WOS关键词	VALIDATION ; INDEX
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/405188
推荐引用方式 GB/T 7714	Wen, Chengli,Zhang, Xu,Li, Yong,et al. An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury[J],2024,19(5).
APA	Wen, Chengli.,Zhang, Xu.,Li, Yong.,Xiao, Wanmeng.,Hu, Qinxue.,...&Lue, Muhan.(2024).An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury.PLOS ONE,19(5).
MLA	Wen, Chengli,et al."An interpretable machine learning model for predicting 28-day mortality in patients with sepsis-associated liver injury".PLOS ONE 19.5(2024).