Objective To evaluate risk factors for postoperative acute renal failure requiring dialysis (ARF-D) after hear valve surgery. Methods Adult patients (age≤18 years) underwent valve surgery with preoperative serum crea...Objective To evaluate risk factors for postoperative acute renal failure requiring dialysis (ARF-D) after hear valve surgery. Methods Adult patients (age≤18 years) underwent valve surgery with preoperative serum creati nine 【 300 μmol / L were included between January 2005 and December 2008. Fifty patients developed ARF-D展开更多
The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper devel...The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper develops a variational Bayesian mixture of factor analyzers （VBMFA） model. This procedure can obtain a lower bound on the Bayesian integral using the Jensen＇s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent. During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model au- tomatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.展开更多
文摘Objective To evaluate risk factors for postoperative acute renal failure requiring dialysis (ARF-D) after hear valve surgery. Methods Adult patients (age≤18 years) underwent valve surgery with preoperative serum creati nine 【 300 μmol / L were included between January 2005 and December 2008. Fifty patients developed ARF-D
基金supported in part by the National Natural Science Foundation of China(60772140)the Program for Cheung Kong Scholarsand Innovative Research Team in University(IRT0645)
文摘The mixture of factor analyzers （MFA） can accurately describe high resolution range profile （HRRP） statistical charac- teristics. But how to determine the proper number of the models is a problem. This paper develops a variational Bayesian mixture of factor analyzers （VBMFA） model. This procedure can obtain a lower bound on the Bayesian integral using the Jensen＇s inequality. An analytical solution of the Bayesian integral could be obtained by a hypothesis that latent variables in the model are indepen- dent. During computing the parameters of the model, birth-death moves are utilized to determine the optimal number of model au- tomatically. Experimental results for measured data show that the VBMFA method has better recognition performance than FA and MFA method.
