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Merge PDF files selecting entire documents or subsections of them. It provides a number of settings to let the user decide what to do in case the original PDF files contain Acro Forms (Acrobat forms) or an outline ( bookmarks ) and it can generate a table of contents , normalize pages size and page margins and add blank pages.
Survival rate is a part of survival analysis.It is the proportion of people in a study or treatment group still alive at a given period of time after diagnosis. It is a method of describing prognosis in certain disease conditions, and can be used for the assessment of standards of therapy.
From a mathematical point of view, by taking values between 0 and 1 or 0% and 100%, CFRs are actually a measure of risk (case fatality risk) – that is, they are a proportion of incidence, although they do not reflect a disease's incidence. They are neither rates, incidence rates, nor ratios (none of which are limited to the range 0–1). They ...
Prognosis (Greek: πρόγνωσις "fore-knowing, foreseeing"; pl.: prognoses) is a medical term for predicting the likelihood or expected development of a disease, including whether the signs and symptoms will improve or worsen (and how quickly) or remain stable over time; expectations of quality of life, such as the ability to carry out daily activities; the potential for complications and ...
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Five-year survival rates can be used to compare the effectiveness of treatments. Use of five-year survival statistics is more useful in aggressive diseases that have a shorter life expectancy following diagnosis, such as lung cancer, and less useful in cases with a long life expectancy, such as prostate cancer.
Human infectious diseases may be characterized by their case fatality rate (CFR), the proportion of people diagnosed with a disease who die from it (cf. mortality rate).It should not be confused with the infection fatality rate (IFR), the estimated proportion of people infected by a disease-causing agent, including asymptomatic and undiagnosed infections, who die from the disease.
Data-driven prognostics usually use pattern recognition and machine learning techniques to detect changes in system states. [3] The classical data-driven methods for nonlinear system prediction include the use of stochastic models such as the autoregressive (AR) model, the threshold AR model, the bilinear model, the projection pursuit, the multivariate adaptive regression splines, and the ...