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	Adaptive Logistic Group Lasso Method for Predicting the No-reflow among the Multiple Types of High-dimensional Variables with Missing Data; Adaptive Logistic Group Lasso Method for Predicting the No-reflow among the Multiple Types of High-dimensional Variables with Missing Data
	Xianglin Yang ; Yunhai Tong ; Xiangfeng Meng ; Shuai Zhao ; Zhi Xu ; Yanjun Li ; Xin Jia ; Shaohua Tan
	2016
关键词	no-reflow EMR adaptive group Lasso k-nearest neighbors(KNN) logistic regression prediction no-reflow EMR adaptive group Lasso k-nearest neighbors(KNN) logistic regression prediction
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 Knearest 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 p...; 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; The Institute of Electrical and Electronics Engineers、IEEE Beijing Section; 5
语种	英语
内容类型	其他
源URL	[http://ir.pku.edu.cn/handle/20.500.11897/479885]
专题	信息科学技术学院
推荐引用方式 GB/T 7714	Xianglin Yang,Yunhai Tong,Xiangfeng Meng,et al. Adaptive Logistic Group Lasso Method for Predicting the No-reflow among the Multiple Types of High-dimensional Variables with Missing Data, Adaptive Logistic Group Lasso Method for Predicting the No-reflow among the Multiple Types of High-dimensional Variables with Missing Data. 2016-01-01.

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