Purpose:Corneal topograph-guided laser subepithelial keratomileusis (LASEK) can effectively correct decentered ablation occurring post laser in situ keratomileusis (LASIK) and to enhance our understanding and diagnosi...Purpose:Corneal topograph-guided laser subepithelial keratomileusis (LASEK) can effectively correct decentered ablation occurring post laser in situ keratomileusis (LASIK) and to enhance our understanding and diagnosis of decentered ablation following LASIK. Methods:Previous studies in the relevant literature are reviewed, and a case report is provided. Results:A patient with high myopia undergoing LASIK in both eyes presented with distorted vision in the left eye, which interfered with the vision in the right eye and caused blurred vision in both eyes. The patient was unable to see objects with both eyes. After receiving corneal topography-guided LASEK,the signs of distorted vision in the left eye and bilateral blurred vision were significantly alleviated,and the patient could see objects with both eyes simultaneously. Conclusion: Clinical ophthalmologists should be aware of the occurrence of decentered ablation after LASIK. Corneal topography-guided LASEK is an efficacious tool for correcting decentered ablation.展开更多
The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper...The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper, we propose a flexible Bayesian Lasso and adaptive Lasso quantile regression by introducing a hierarchical model framework approach to enable exact inference and shrinkage of an unimportant coefficient to zero. The error distribution is assumed to be an infinite mixture of Gaussian densities. We have theoretically investigated and numerically compared our proposed methods with Flexible Bayesian quantile regression (FBQR), Lasso quantile regression (LQR) and quantile regression (QR) methods. Simulations and real data studies are conducted under different settings to assess the performance of the proposed methods. The proposed methods perform well in comparison to the other methods in terms of median mean squared error, mean and variance of the absolute correlation criterions. We believe that the proposed methods are useful practically.展开更多
The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on...The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.展开更多
在分位回归的响应变量存在不可忽略缺失的情形下,本文引入半参数指数倾斜模型刻画应答概率,在此基础上提出3种卷积平滑分位数回归估计方程:逆概率加权(inverse probability weighting,IPW)、估计方程插补(estimation equation imputatio...在分位回归的响应变量存在不可忽略缺失的情形下,本文引入半参数指数倾斜模型刻画应答概率,在此基础上提出3种卷积平滑分位数回归估计方程:逆概率加权(inverse probability weighting,IPW)、估计方程插补(estimation equation imputation, EEI)和增强逆概率加权(augmented IPW,AIPW),并在经验似然框架下得到倾斜参数和分位回归系数的估计量.本文在理论上证明3种分位回归估计量等价的渐近正态性和对应调整对数经验似然比函数的渐近χ^(2)性质.数值模拟比较上述估计量的有限样本表现,验证估计量的稳健性.本文所提出的方法被应用于CD4 (cluster of differentiation4)数据分析,考察不同治疗组中缺失机制的差异以及基线和前期的CD4和CD8细胞水平对当期CD4细胞水平的影响.展开更多
Randomized controlled trials(RCTs)provide optimal evidence of the effectiveness and safety of a new drug,a new medical device,or a new therapeutic strategy with the necessary scientific design[1].Traditional electroni...Randomized controlled trials(RCTs)provide optimal evidence of the effectiveness and safety of a new drug,a new medical device,or a new therapeutic strategy with the necessary scientific design[1].Traditional electronic data collection(EDC)systems for clinical trials primarily focus on data entry,validation,and compliance monitoring.However,their reliance on centralized architectures introduces vulnerabilities in data security and integrity。展开更多
Background Corneal stiffness abnormalities play an important role in the onset and progression of keratoconus.However,the limited availability of specialty devices for measuring corneal stiffness restricts their appli...Background Corneal stiffness abnormalities play an important role in the onset and progression of keratoconus.However,the limited availability of specialty devices for measuring corneal stiffness restricts their application in clinical practice.This study aimed to develop a machine learning(ML)model that can predict corneal stiffness based on ocular structures and investigate its efficacy in diagnosing keratoconus.Methods This retrospective study enrolled healthy individuals and keratoconus patients at the Zhongshan Ophthalmic Center from June 2018 to June 2021.Eleven features,including ocular structural parameters,intraocular pressure(IOP),and age were used to train ML regression models for predicting the stiffness parameter at first applanation(SP-A1)and the Corvis biomechanical index for Chinese populations(cCBI)measured by a Corvis ST device.Mean absolute errors(MAEs)and the area under the receiver operating characteristic curve(AUC)were used to evaluate the performance of the models.The diagnostic efficacy of the predicted SP-A1 and cCBI for keratoconus was evaluated by the AUC,net reclassification index(NRI),and integrated discrimination improvement(IDI).Results A total of 1,523 eyes were involved,of which 601 were diagnosed with keratoconus.The MAEs of the SP-A1 prediction were similar in cross-validation(8.95 mmHg/mm)and testing(10.65 mmHg/mm).The R2 value for the SP-A1 prediction exceeded 0.7,indicating that the performance was clinically acceptable.The AUC for the cCBI prediction was 0.935(95%CI 0.906-0.963).The top three predictors for SP-A1 and cCBI were IOP,keratometry,and central corneal thickness.The addition of the predicted SP-A1 and cCBI significantly improved model performance in diagnosing keratoconus,with NRI of 0.607(95%CI 0.367-0.812)and 0.188(95%CI−0.022-0.398),and IDI of 0.028(95%CI 0.006-0.048)and 0.045(95%CI 0.018-0.072),respectively.Conclusion Our models predicted SP-A1 and cCBI relatively accurately in keratoconus and normal corneas.Moreover,the predicted SP-A1 and cCBI values significantly contributed to the diagnosis of keratoconus.These models could provide a potential alternative for evaluating corneal stiffness and thus facilitate keratoconus screening.展开更多
文摘Purpose:Corneal topograph-guided laser subepithelial keratomileusis (LASEK) can effectively correct decentered ablation occurring post laser in situ keratomileusis (LASIK) and to enhance our understanding and diagnosis of decentered ablation following LASIK. Methods:Previous studies in the relevant literature are reviewed, and a case report is provided. Results:A patient with high myopia undergoing LASIK in both eyes presented with distorted vision in the left eye, which interfered with the vision in the right eye and caused blurred vision in both eyes. The patient was unable to see objects with both eyes. After receiving corneal topography-guided LASEK,the signs of distorted vision in the left eye and bilateral blurred vision were significantly alleviated,and the patient could see objects with both eyes simultaneously. Conclusion: Clinical ophthalmologists should be aware of the occurrence of decentered ablation after LASIK. Corneal topography-guided LASEK is an efficacious tool for correcting decentered ablation.
文摘The selection of predictors plays a crucial role in building a multiple regression model. Indeed, the choice of a suitable subset of predictors can help to improve prediction accuracy and interpretation. In this paper, we propose a flexible Bayesian Lasso and adaptive Lasso quantile regression by introducing a hierarchical model framework approach to enable exact inference and shrinkage of an unimportant coefficient to zero. The error distribution is assumed to be an infinite mixture of Gaussian densities. We have theoretically investigated and numerically compared our proposed methods with Flexible Bayesian quantile regression (FBQR), Lasso quantile regression (LQR) and quantile regression (QR) methods. Simulations and real data studies are conducted under different settings to assess the performance of the proposed methods. The proposed methods perform well in comparison to the other methods in terms of median mean squared error, mean and variance of the absolute correlation criterions. We believe that the proposed methods are useful practically.
基金The work was financially supported by National Natural Science Fund of China,specific grant numbers were 61371143 and 61662033initials of authors who received the grants were respectively Z.YM,H.L,and the URLs to sponsors’websites was http://www.nsfc.gov.cn/.This paper was supported by National Natural Science Fund of China(Grant Nos.61371143,61662033).
文摘The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not ideal.Haze prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of haze.In order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated learning.Minimum Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level features.eXtreme Gradient Boosting algorithm is adopted to fuse low-level and high-level features,as well as predict haze.Establish PM2.5 concentration pollution grade classification index,and grade the forecast data.The expert experience knowledge is utilized to assist the optimization of the pre-warning results.The experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.
文摘在分位回归的响应变量存在不可忽略缺失的情形下,本文引入半参数指数倾斜模型刻画应答概率,在此基础上提出3种卷积平滑分位数回归估计方程:逆概率加权(inverse probability weighting,IPW)、估计方程插补(estimation equation imputation, EEI)和增强逆概率加权(augmented IPW,AIPW),并在经验似然框架下得到倾斜参数和分位回归系数的估计量.本文在理论上证明3种分位回归估计量等价的渐近正态性和对应调整对数经验似然比函数的渐近χ^(2)性质.数值模拟比较上述估计量的有限样本表现,验证估计量的稳健性.本文所提出的方法被应用于CD4 (cluster of differentiation4)数据分析,考察不同治疗组中缺失机制的差异以及基线和前期的CD4和CD8细胞水平对当期CD4细胞水平的影响.
基金supported by the National Natural Science Foundation of China(92368205 and 82301265)the Guangdong Provincial Natural Science Foundation for Progressive Young Scholars(2023A1515030170)+3 种基金the Science and Technology Planning Projects of Guangdong Province(2018B010109008)the Science and Technology Planning Project of the Guangzhou Municipal Health Commission(2024A031004)the Basic Scientific Research Projects of Sun Yat-sen University(23ykcxqt002)the China Postdoctoral Science Foundation(2023M734047).
文摘Randomized controlled trials(RCTs)provide optimal evidence of the effectiveness and safety of a new drug,a new medical device,or a new therapeutic strategy with the necessary scientific design[1].Traditional electronic data collection(EDC)systems for clinical trials primarily focus on data entry,validation,and compliance monitoring.However,their reliance on centralized architectures introduces vulnerabilities in data security and integrity。
基金supported by the Science and Technology Planning Projects of Guangdong Province(Grant No.2018B010109008)the Guangzhou Key Laboratory Project(Grant No.202002010006)the Key-Area Research and Development of Guangdong Province(Grant No.2020B1111190001).
文摘Background Corneal stiffness abnormalities play an important role in the onset and progression of keratoconus.However,the limited availability of specialty devices for measuring corneal stiffness restricts their application in clinical practice.This study aimed to develop a machine learning(ML)model that can predict corneal stiffness based on ocular structures and investigate its efficacy in diagnosing keratoconus.Methods This retrospective study enrolled healthy individuals and keratoconus patients at the Zhongshan Ophthalmic Center from June 2018 to June 2021.Eleven features,including ocular structural parameters,intraocular pressure(IOP),and age were used to train ML regression models for predicting the stiffness parameter at first applanation(SP-A1)and the Corvis biomechanical index for Chinese populations(cCBI)measured by a Corvis ST device.Mean absolute errors(MAEs)and the area under the receiver operating characteristic curve(AUC)were used to evaluate the performance of the models.The diagnostic efficacy of the predicted SP-A1 and cCBI for keratoconus was evaluated by the AUC,net reclassification index(NRI),and integrated discrimination improvement(IDI).Results A total of 1,523 eyes were involved,of which 601 were diagnosed with keratoconus.The MAEs of the SP-A1 prediction were similar in cross-validation(8.95 mmHg/mm)and testing(10.65 mmHg/mm).The R2 value for the SP-A1 prediction exceeded 0.7,indicating that the performance was clinically acceptable.The AUC for the cCBI prediction was 0.935(95%CI 0.906-0.963).The top three predictors for SP-A1 and cCBI were IOP,keratometry,and central corneal thickness.The addition of the predicted SP-A1 and cCBI significantly improved model performance in diagnosing keratoconus,with NRI of 0.607(95%CI 0.367-0.812)and 0.188(95%CI−0.022-0.398),and IDI of 0.028(95%CI 0.006-0.048)and 0.045(95%CI 0.018-0.072),respectively.Conclusion Our models predicted SP-A1 and cCBI relatively accurately in keratoconus and normal corneas.Moreover,the predicted SP-A1 and cCBI values significantly contributed to the diagnosis of keratoconus.These models could provide a potential alternative for evaluating corneal stiffness and thus facilitate keratoconus screening.
