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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 ...
Prognostic factors are often used in combination with predictive variables in therapeutics studies, to examine how effective different treatments are in curing specific diseases or cancer. As opposed to predictive biomarkers, prognostic do not rely on any explanatory variables, thus allowing for independent examination of the underlying disease ...
It is necessary to distinguish between disease-related and drug-related biomarkers.Disease-related biomarkers give an indication of the probable effect of treatment on patient (risk indicator or predictive biomarkers), if a disease already exists (diagnostic biomarker), or how such a disease may develop in an individual case regardless of the type of treatment (prognostic biomarker).
A predictive gene signature unlike a prognostic gene signature can be a target for therapy. [17] The information predictive signatures provide are more rigorous than that of prognostic signatures as they are based on treatment groups with therapeutic intervention on the likely benefit from treatment, completely independent of prognosis. [24]
The positive predictive value (PPV), or precision, is defined as = + = where a "true positive" is the event that the test makes a positive prediction, and the subject has a positive result under the gold standard, and a "false positive" is the event that the test makes a positive prediction, and the subject has a negative result under the gold standard.
One of the most frequently seen prognostic markers is DNA methylation, primarily methylation of CpG islands, where cytosines in CpG dinucleotides can be methylated to form 5-methylcytosines. A panel of epigenetic methylation marker has been explored for prognosis of ovarian cancer, and it is reported that the panel exhibited high specificity ...
Real prognostic variables are usually known with some uncertainty, may be difficult to measure, and their correlation to the system's state of health may not be exact. Examples of prognostic variables are the age of a vehicle and its odometer reading: the older a car is, and the longer it has been driven, the more worn it can be expected to be ...
Prognostic equation - in the context of physical (and especially geophysical) simulation, a prognostic equation predicts the value of variables for some time in the ...