QQ客服 官方微博 反馈留言

	Adaptive Logistic Group Lasso Method for Predicting the No-reflow among the Multiple Types of High-dimensional Variables with Missing Data
	Yang, Xianglin ; Tong, Yunhai ; Meng, Xiangfeng ; Zhao, Shuai ; Xu, Zhi ; Li, Yanjun ; Jia, Xin ; Tan, Shaohua
	2016
关键词	no-reflow EMR adaptive group Lasso k-nearest neighbors (KNN) logistic regression prediction PERCUTANEOUS CORONARY INTERVENTION ELEVATION MYOCARDIAL-INFARCTION STATIN THERAPY REGRESSION SELECTION
英文摘要	The prediction of no-reflow phenomenon aroused much attention, because of its independent association with increased in-hospital mortality, malignant arrhythmias, and cardiac failure. Many studies on prediction of no-reflow were carried out focusing on only few predictors. As big data era has been coming, high-dimensional predictors are available for prediction. However, as a common problem, big data analytics in healthcare from the electronic medical record (EMR) system is faced with many challenges, such as missing data processing, multiple types of variables processing and the high-dimensional data prediction. A general method based on improved weighted K-nearest neighbors and adaptive logistic group Lasso was proposed for predicting the no-reflow after cardiac surgery among the multiple types of variables with missing data. Compared with logistic regression, Lasso method, and artificial neural network method, our method has lower misclassification error rate and less complex model for no-reflow prediction, especially when predicting among multiple types of variables with missing data.; National Natural Science Foundation of China [61401417]; State Key Laboratory of Space Medicine Fundamentals and application [SMFA12B09, SMFA13B03]; CPCI-S(ISTP); 1085-1089
语种	英语
出处	7th IEEE International Conference on Software Engineering and Service Science (ICSESS)
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/470198]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Yang, Xianglin,Tong, Yunhai,Meng, Xiangfeng,et al. Adaptive Logistic Group Lasso Method for Predicting the No-reflow among the Multiple Types of High-dimensional Variables with Missing Data. 2016-01-01.

个性服务

查看访问统计

相关权益政策

暂无数据

收藏/分享

除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。