Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates havi...Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.展开更多
功能超网络广泛地应用于脑疾病诊断和分类研究中,而现有的关于超网络创建的研究缺乏解释分组效应的能力或者仅考虑到脑区间组级的信息,这样构建的脑功能超网络会丢失一些有用的连接或包含一些虚假的信息,因此,考虑到脑区间的组结构问题...功能超网络广泛地应用于脑疾病诊断和分类研究中,而现有的关于超网络创建的研究缺乏解释分组效应的能力或者仅考虑到脑区间组级的信息,这样构建的脑功能超网络会丢失一些有用的连接或包含一些虚假的信息,因此,考虑到脑区间的组结构问题,引入sparse group Lasso(sgLasso)方法进一步改善超网络的创建。首先,利用sgLasso方法进行超网络创建;然后,引入两组超网络特有的属性指标进行特征提取以及特征选择,这些指标分别是基于单一节点的聚类系数和基于一对节点的聚类系数;最后,将特征选择后得到的两组有显著差异的特征通过多核学习进行特征融合和分类。实验结果表明,所提方法经过多特征融合取得了87.88%的分类准确率。该结果表明为了改善脑功能超网络的创建,需要考虑到组信息,但不能逼迫使用整组信息,可以适当地对组结构进行扩展。展开更多
<strong>Objective</strong>: This paper aims to explore clinical status and related influence factors of pressure injury (PI) in the elderly inpatients with kidney disease, so as to provide reference for th...<strong>Objective</strong>: This paper aims to explore clinical status and related influence factors of pressure injury (PI) in the elderly inpatients with kidney disease, so as to provide reference for the prevention and treatment of PI in the elderly inpatients with kidney disease. <strong>Methods</strong>: Retrospective collection method is adopted to collect 158 clinical cases of the elderly inpatients with kidney disease aged ≥ 60 in the Nephrology Department, the First Affiliated Hospital of Jinan University from January 2017 to December 2019, and then least absolute shrinkage and selection Operator (LASSO) regression analysis is used to analyze 17 possible influence factors;finally Logistic regression model is established to analyze and screen influence factors of risk. <strong>Results</strong>: 1) Among 158 elderly inpatients with medium and high risk of PI, the incidence of PI is 20.25%;the most common stage of injury is stage I (42.5%);sacrococcygeal (60%) is the high-risk site of pressure injury. 2) LASSO regression analysis shows that history of present respiratory infection/respiratory failure (<em>β </em>= 1.2714. <em>P</em> < 0.05) and hospitalization time (<em>β</em> = 0.4177. <em>P </em>< 0.05) are independent factors influencing PI risk in the elderly inpatients with kidney disease. <strong>Concl</strong><strong>usio</strong><strong>n</strong>: The elderly patients with kidney disease and PI risk are the high incidence population of hospital acquired PI;for the elderly inpatients with kidney disease and having respiratory infection history or respiratory failure, prolonged hospitalization will significantly increase the risk of PI. Therefore, targeted preventive and control measures should be taken to reduce the incidence of PI.展开更多
文摘Least Absolute Shrinkage and Selection Operator (LASSO) is used for variable selection as well as for handling the multicollinearity problem simultaneously in the linear regression model. LASSO produces estimates having high variance if the number of predictors is higher than the number of observations and if high multicollinearity exists among the predictor variables. To handle this problem, Elastic Net (ENet) estimator was introduced by combining LASSO and Ridge estimator (RE). The solutions of LASSO and ENet have been obtained using Least Angle Regression (LARS) and LARS-EN algorithms, respectively. In this article, we proposed an alternative algorithm to overcome the issues in LASSO that can be combined LASSO with other exiting biased estimators namely Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator. Further, we examine the performance of the proposed algorithm using a Monte-Carlo simulation study and real-world examples. The results showed that the LARS-rk and LARS-rd algorithms,?which are combined LASSO with r-k class estimator and r-d class estimator,?outperformed other algorithms under the moderated and severe multicollinearity.
文摘功能超网络广泛地应用于脑疾病诊断和分类研究中,而现有的关于超网络创建的研究缺乏解释分组效应的能力或者仅考虑到脑区间组级的信息,这样构建的脑功能超网络会丢失一些有用的连接或包含一些虚假的信息,因此,考虑到脑区间的组结构问题,引入sparse group Lasso(sgLasso)方法进一步改善超网络的创建。首先,利用sgLasso方法进行超网络创建;然后,引入两组超网络特有的属性指标进行特征提取以及特征选择,这些指标分别是基于单一节点的聚类系数和基于一对节点的聚类系数;最后,将特征选择后得到的两组有显著差异的特征通过多核学习进行特征融合和分类。实验结果表明,所提方法经过多特征融合取得了87.88%的分类准确率。该结果表明为了改善脑功能超网络的创建,需要考虑到组信息,但不能逼迫使用整组信息,可以适当地对组结构进行扩展。
文摘<strong>Objective</strong>: This paper aims to explore clinical status and related influence factors of pressure injury (PI) in the elderly inpatients with kidney disease, so as to provide reference for the prevention and treatment of PI in the elderly inpatients with kidney disease. <strong>Methods</strong>: Retrospective collection method is adopted to collect 158 clinical cases of the elderly inpatients with kidney disease aged ≥ 60 in the Nephrology Department, the First Affiliated Hospital of Jinan University from January 2017 to December 2019, and then least absolute shrinkage and selection Operator (LASSO) regression analysis is used to analyze 17 possible influence factors;finally Logistic regression model is established to analyze and screen influence factors of risk. <strong>Results</strong>: 1) Among 158 elderly inpatients with medium and high risk of PI, the incidence of PI is 20.25%;the most common stage of injury is stage I (42.5%);sacrococcygeal (60%) is the high-risk site of pressure injury. 2) LASSO regression analysis shows that history of present respiratory infection/respiratory failure (<em>β </em>= 1.2714. <em>P</em> < 0.05) and hospitalization time (<em>β</em> = 0.4177. <em>P </em>< 0.05) are independent factors influencing PI risk in the elderly inpatients with kidney disease. <strong>Concl</strong><strong>usio</strong><strong>n</strong>: The elderly patients with kidney disease and PI risk are the high incidence population of hospital acquired PI;for the elderly inpatients with kidney disease and having respiratory infection history or respiratory failure, prolonged hospitalization will significantly increase the risk of PI. Therefore, targeted preventive and control measures should be taken to reduce the incidence of PI.