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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.
In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances.
The template for any binary confusion matrix uses the four kinds of results discussed above (true positives, false negatives, false positives, and true negatives) along with the positive and negative classifications.
A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the performance of a binary classifier model (can be used for multi class classification as well) at varying threshold values. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR) at each threshold setting.
The zones of the lung divide the lung into four vertical regions, based upon the relationship between the pressure in the alveoli (PA), in the arteries (Pa), in the veins (Pv) and the pulmonary interstitial pressure (Pi): Zone 1: PA > Pa > Pv. Zone 2: Pa > PA > Pv. Zone 3: Pa > Pv > PA.
Pre-test probability and post-test probability (alternatively spelled pretest and posttest probability) are the probabilities of the presence of a condition (such as a disease) before and after a diagnostic test, respectively. Post-test probability, in turn, can be positive or negative, depending on whether the test falls out as a positive test ...
A PV diagram plots the change in pressure P with respect to volume V for some process or processes. Typically in thermodynamics, the set of processes forms a cycle, so that upon completion of the cycle there has been no net change in state of the system; i.e. the device returns to the starting pressure and volume.
The Frank–Starling mechanism can be explained on the basis of preload. As the heart fills with more blood than usual, there is an increase in the load experienced by each myocyte. This stretches the muscle fibers, increasing the affinity of troponin C to Ca 2+ ions, causing a greater number of cross-bridges to form within the muscle fibers ...