Foghclancy7134

Hypertension has become a major public health issue as the prevalence and risk of premature death and disability among adults due to hypertension has increased globally. The main objective is to characterize the risk factors of hypertension among adults in Bangladesh using machine learning (ML) algorithms.

The hypertension data was derived from Bangladesh demographic and health survey, 2017-18, which included 6965 people aged 35 and above. Two most promising risk factor identification methods, namely least absolute shrinkage operator (LASSO) and support vector machine recursive feature elimination (SVMRFE) are implemented to detect the critical risk factors of hypertension. Additionally, four well-known ML algorithms as artificial neural network, decision tree, random forest, and gradient boosting (GB) have been used to predict hypertension. Performance scores of these algorithms were evaluated by accuracy, precision, recall, F-measure, and area under the curve (AUC).

The results clarify that age, BMI, wealth index, working status, and marital status for LASSO and age, BMI, marital status, diabetes and region for SVMRFE appear to be the top-most five significant risk factors for hypertension. Our findings reveal that the combination of SVMRFE-GB gives the maximum accuracy (66.98%), recall (97.92%), F-measure (78.99%), and AUC (0.669) compared to others.

GB-based algorithm confirms the best performer for prediction of hypertension, at an early stage in Bangladesh. Therefore, this study highly suggests that the policymakers make proper judgments for controlling hypertension using SVMRFE-GB-based combination to save time and reduce cost for Bangladeshi adults.

GB-based algorithm confirms the best performer for prediction of hypertension, at an early stage in Bangladesh. Therefore, this study highly suggests that the policymakers make proper judgments for controlling hypertension using SVMRFE-GB-based combination to save time and reduce cost for Bangladeshi adults.

Long COVID is the collective term to denote persistence of symptoms in those who have recovered from SARS-CoV-2 infection.

WE searched the pubmed and scopus databases for original articles and reviews. Based on the search result, in this review article we are analyzing various aspects of Long COVID.

Fatigue, cough, chest tightness, breathlessness, palpitations, myalgia and difficulty to focus are symptoms reported in long COVID. It could be related to organ damage, post viral syndrome, post-critical care syndrome and others. Clinical evaluation should focus on identifying the pathophysiology, followed by appropriate remedial measures. In people with symptoms suggestive of long COVID but without known history of previous SARS-CoV-2 infection, serology may help confirm the diagnosis.

This review will helps the clinicians to manage various aspects of Long COVID.

This review will helps the clinicians to manage various aspects of Long COVID.

Despite the trend of rising Emergency Department (ED) visits over the past decade, researchers have observed drastic declines in number of ED visits due to the COVID-19 pandemic. The purpose of the current study was to examine the impact of the COVID-19 pandemic and governor mandated Stay at Home Order on ED super utilizers.

This was a retrospective chart review of patients presenting to the 12 emergency departments of the Franciscan Mission of Our Lady Hospital System in Louisiana between January 1, 2018 and December 31, 2020. Patients who were 18 years of age or older and had four ED visits within a one-year period (2018, 2019, or 2020) were classified as super-utilizers. We examined number and category of visits for the baseline period (January 2018 - March 2020), the governor's Stay at Home Order, and the subsequent Reopening Phases through December 31, 2020.

The number of visits by super utilizers decreased by over 16% when the Stay at Home Order was issued. The average number of visits per week rote ED use.

Significant declines in emergent visits raise concerns that individuals who needed ED treatment did not seek it due to COVID-19. click here However, the finding that super utilizers with non-emergent visits continued to visit the ED less after the Stay at Home Order was lifted raises questions for future research that may inform policy and interventions for inappropriate ED use.It is known that coordination between joint movements is crucial for the achievement of motor tasks and has been studied extensively. Especially, in sports biomechanics, researchers are interested in determining which joint movements are coordinated to achieve a motor task. However, this issue cannot be easily addressed with the methods employed in previous studies. Therefore, we aimed to propose a method for identifying joint coordination. Subsequently, we examined which joint movements were coordinated using accurate overhead throwing, which required reduction in vertical hand velocity variability. Fourteen baseball players participated by attempting throwing using a motion capture system. The index of coordination for each joint movement and the effect of deviation of one joint movement on vertical hand velocity were quantified. Our results showed that the shoulder internal/external rotation angle (θ1-IE) and the other joint movements or the shoulder horizontal flexion/extension angular velocity (ω1-FE) and the other joint movements were coordinated. These results could be explained by the fact that the effects of the deviation of the shoulder internal rotation angle (θ1-I) and shoulder horizontal flexion angular velocity (ω1-F) on vertical hand velocity were larger than those of the other joint movements. This meant that it was necessary to cancel the deviations of θ1-IE and ω1-FE by the other joint movements. These findings indicate that the method proposed in this study enables the identification of which joint movements are coordinated in multiple degrees of freedom.This article addresses extraction of physically meaningful information from STEM EELS and EDX spectrum-images using methods of Multivariate Statistical Analysis. The problem is interpreted in terms of data distribution in a multi-dimensional factor space, which allows for a straightforward and intuitively clear comparison of various approaches. A new computationally efficient and robust method for finding physically meaningful endmembers in spectrum-image datasets is presented. The method combines the geometrical approach of Vertex Component Analysis with the statistical approach of Bayesian inference. The algorithm is described in detail at an example of EELS spectrum-imaging of a multi-compound CMOS transistor.