干旱区生态环境知识资源中心(Arid): Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit

Arid

DOI	10.1371/journal.pone.0280606
	Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit
	Huang, Tianzhi; Le, Dejin; Yuan, Lili; Xu, Shoujia; Peng, Xiulan
通讯作者	Xu, SJ
来源期刊	PLOS ONE
ISSN	1932-6203
出版年	2023
卷号	18 期号:1
英文摘要	BackgroundsThe in-hospital mortality in lung cancer patients admitted to intensive care unit (ICU) is extremely high. This study intended to adopt machine learning algorithm models to predict in-hospital mortality of critically ill lung cancer for providing relative information in clinical decision-making. MethodsData were extracted from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) for a training cohort and data extracted from the Medical Information Mart for eICU Collaborative Research Database (eICU-CRD) database for a validation cohort. Logistic regression, random forest, decision tree, light gradient boosting machine (LightGBM), eXtreme gradient boosting (XGBoost), and an ensemble (random forest+LightGBM+XGBoost) model were used for prediction of in-hospital mortality and important feature extraction. The AUC (area under receiver operating curve), accuracy, F1 score and recall were used to evaluate the predictive performance of each model. Shapley Additive exPlanations (SHAP) values were calculated to evaluate feature importance of each feature. ResultsOverall, there were 653 (24.8%) in-hospital mortality in the training cohort, and 523 (21.7%) in-hospital mortality in the validation cohort. Among the six machine learning models, the ensemble model achieved the best performance. The top 5 most influential features were the sequential organ failure assessment (SOFA) score, albumin, the oxford acute severity of illness score (OASIS) score, anion gap and bilirubin in random forest and XGBoost model. The SHAP summary plot was used to illustrate the positive or negative effects of the top 15 features attributed to the XGBoost model. ConclusionThe ensemble model performed best and might be applied to forecast in-hospital mortality of critically ill lung cancer patients, and the SOFA score was the most important feature in all models. These results might offer valuable and significant reference for ICU clinicians' decision-making in advance.
类型	Article
语种	英语
开放获取类型	gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:000951578400001
WOS关键词	FAILURE ASSESSMENT SCORE ; SOFA SCORE ; DISEASE ; MANAGEMENT ; DIAGNOSIS ; ACCURACY ; OUTCOMES ; SEPSIS ; ADULTS
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/2XILL650/398111
推荐引用方式 GB/T 7714	Huang, Tianzhi,Le, Dejin,Yuan, Lili,et al. Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit[J],2023,18(1).
APA	Huang, Tianzhi,Le, Dejin,Yuan, Lili,Xu, Shoujia,&Peng, Xiulan.(2023).Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit.PLOS ONE,18(1).
MLA	Huang, Tianzhi,et al."Machine learning for prediction of in-hospital mortality in lung cancer patients admitted to intensive care unit".PLOS ONE 18.1(2023).