It has been estimated that hypertension is the cause of approximately 13% of all deaths worldwide each year. 1 In light of the world's population growth and aging demographic, hypertension is a global burden, along with other cardiovascular and age-related diseases. 2,3 Early intervention with lifestyle modifications and treatment of "prehypertension" may reduce the incidence and long-term consequences of clinical hypertension. [4][5][6][7] Recent guidelines lowered the recommended thresholds for diagnosing hypertension or abnormal "elevated blood pressure (BP)" and the BP goal during antihypertensive therapy. 8-10 Therefore, the ability to predict an individual's risk of developing hypertension would be helpful for clinicians. They could then plan and prescribe personalized lifestyle modifications or make therapeutic decisions designed to prevent or postpone the development of hypertension. There are several models available to predict the risk of new-onset hypertension; these have been developed in Western and Asian countries using traditional statistical methods (eg, Cox regression or logistic regression). 11,12 Arterial stiffness is increasingly being recognized as making an important contribution to increases in systolic BP (SBP) and the development of hypertension in general populations, independent of traditional hypertension risk factors. [13][14][15][16] In addition, arterial
AbstractHypertension is a significant public health issue. The ability to predict the risk of developing hypertension could contribute to disease prevention strategies. This study used machine learning techniques to develop and validate a new risk prediction model for new-onset hypertension. In Japan, Industrial Safety and Health Law requires employers to provide annual health checkups to their employees. We used 2005-2016 health checkup data from 18 258 individuals, at the time of hypertension diagnosis [Year (0)] and in the two previous annual visits [Year (−1) and Year (−2)].Data were entered into models based on machine learning methods (XGBoost and ensemble) or traditional statistical methods (logistic regression). Data were randomly split into a derivation set (75%, n = 13 694) used for model construction and development, and a validation set (25%, n = 4564) used to test performance of the derived models. The best predictor in the XGBoost model was systolic blood pressure during cardio-ankle vascular index measurement at Year (−1). Area under the receiver operator characteristic curve values in the validation cohort were 0.877, 0.881, and 0.859 for the XGBoost, ensemble, and logistic regression models, respectively. We have developed a highly precise prediction model for future hypertension using machine learning methods in a general normotensive population. This could be used to identify at-risk individuals and facilitate earlier non-pharmacological intervention to prevent the future development of hypertension.
A new inspection system for stencil mask using transmission electron beam (B-beam) has been developed to detect defects on masks for Electron Projection Lithography (EPL) and Low Energy E-beam Proximity projection Lithography (LEEPL) for 65 nm design rule and beyond [1]• For high-performance image acquisition, the combination of multi-line Time Delay integration (TDI) -CCD camera and electron optic system (EOS) have been achieved very wide field-of-view and accurate imaging in this system.In Image Processing Unit, "Multi Algorithm Processing" is used for defect detection. One of "Multi Algorithm Processing" focuses on defects at corners of patterns. This is a new and very flexible algorithm to detect corner defects. It realizes very high detection performance compared with conventional Die-To-Database inspection. The minimum detectable defect size is smaller than 1 pixel. The pixel size is 50 nm for EPL mask and 30 nm for LEEPL mask. The performance of the system also has been confirmed using resist pattern wafer inspection results after EPL and LEEPL printing.
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