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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 ...
Microsoft Word is a word processing program developed by Microsoft.It was first released on October 25, 1983, [12] under the name Multi-Tool Word for Xenix systems. [13] [14] [15] Subsequent versions were later written for several other platforms including: IBM PCs running DOS (1983), Apple Macintosh running the Classic Mac OS (1985), AT&T UNIX PC (1985), Atari ST (1988), OS/2 (1989 ...
Prognostic markers are biomarkers used to measure the progress of a disease in the patient sample. [1] Prognostic markers are useful to stratify the patients into groups, guiding towards precise medicine discovery. The widely used prognostic markers in cancers include stage, size, grade, node and metastasis. In addition to these common markers ...
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.
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The term template, when used in the context of word processing software, refers to a sample document that has already some details in place; those can (that is added/completed, removed or changed, differently from a fill-in-the-blank of the approach as in a form) either by hand or through an automated iterative process, such as with a software assistant.
In a prediction rule study, investigators identify a consecutive group of patients who are suspected of having a specific disease or outcome. The investigators then obtain a standard set of clinical observations on each patient and a test or clinical follow-up to define the true state of the patient.
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 ...