The pandemic caused by coronavirus COVID-19 has already had a massive impact in our societies terms of health, economy, and social distress. One the most common symptoms are lung problems like pneumonia, which can be detected using X-ray images. On other hand, popularity Machine Learning models grown exponentially recent years Deep techniques have become state-of-the-art for image classificatio...

Background: Early prediction of the outcome situation of COVID-19 patients can decrease mortality risk by assuring efficient resource allocation and treatment planning. This study introduces a very accurate and fast system for the prediction of COVID-19 outcomes using demographic, vital signs, and laboratory blood test data. Methods: In this analytic study, which is done from May 2020 to June ...

Mortality prediction is an important problem in the intensive care unit (ICU) because it is helpful for understanding patients’ evolving severity, quality of care, and comparing treatments. Most ICU mortality models primarily consider structured data and physiological waveforms (Le Gall et al., 1993). An important limitation of these structured data approaches is that they miss a lot of vital i...

BACKGROUND Vascular calcified plaque, a measure of subclinical cardiovascular disease (CVD), is unlikely to be limited to a single vascular bed in patients with multiple risk factors. Consideration of vascular calcified plaque as a global phenomenon may allow for a more accurate assessment of the CVD burden. The aim of this study was to examine the utility of a combined vascular calcified plaqu...

AIM To investigate whether plasma dimethylglycine was associated with and improved risk prediction of mortality among patients with coronary heart disease (CHD). METHODS By Cox modelling, we explored the association between plasma dimethylglycine and mortality in two independent cohorts of patients with suspected stable angina pectoris (SAP) (n = 4156) and acute myocardial infarction (AMI) (n...

OBJECTIVE The artificial neural network model is a nonlinear technology useful for complex pattern recognition problems. This study aimed to develop a method to select risk variables and predict mortality after cardiac surgery by using artificial neural networks. METHODS Prospectively collected data from 18,362 patients undergoing cardiac surgery at 128 European institutions in 1995 (the Euro...

Forced vital capacity (FVC) measures lung function and predicts coronary heart disease (CHD); whether it provides additive prediction over CHD risk factors has not been established. We examined whether FVC adds to the prediction of all-cause mortality provided by Framingham Risk Score (FRS) alone. We examined 5,485 (61.1 million projected) nonsmoking adults from the USA who were aged 20-79 yrs....