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Paper 1
Published in Aalia Khan, Ramsey Jabbour, Almas Rehman, nMRCGP Applied Knowledge Test Study Guide, 2021
Aalia Khan, Ramsey Jabbour, Almas Rehman
What is the definition of specificity of a screening test? The true positive rateThe negative predictive valueThe false positive rateThe true negative rateThe false negative rate
Academic Viva
Published in Tjun Tang, Elizabeth O'Riordan, Stewart Walsh, Cracking the Intercollegiate General Surgery FRCS Viva, 2020
True-positive rate: Proportion of subjects with the disorder who will have a positive result.SnNout: highly sensitive test − negative result will rule out the disorder.
Results and Discussion
Published in Arwa Ahmed Gasm Elseid, Alnazier Osman Mohammed Hamza, Computer-Aided Glaucoma Diagnosis System, 2020
Arwa Ahmed Gasm Elseid, Alnazier Osman Mohammed Hamza
ROC graphs are constructed by plotting the true positive rate (TPR) against the false positive rate. Figure 5.29 identifies a number of regions of interest in an ROC graph. The diagonal line from the bottom-left corner to the top-right corner shows the classifier’s performance. In the extreme case, denoted by the point in the bottom-left corner, a conservative classification model will classify all instances as negative and it will not commit any false positives, but it will also not obtain any true positives. The region of classifiers’ performance appears at the top of the graph. These classifiers have a good true positive rate, but they also have a number of false positive errors. When the classifier is at the top-right corner it means that it classifies every instance as positive. In this situation, the classifier will not miss any true positives, but it will also miss a very large number of false positives. If the classifiers fall to the right of the random performance line, this mean it has a performance worse than random performance, due to it producing more false positive than true positive responses. However, because ROC graphs are symmetrical along the random performance line, the point in the top-left corner denotes perfect classification: 100% true positive rate and 0% false positive rate.
Rapid detection of hot-spots via tensor decomposition with applications to crime rate data
Published in Journal of Applied Statistics, 2022
Yujie Zhao, Hao Yan, Sarah Holte, Yajun Mei
Next, let us compare the performance on hot-spots localization of these methods, i.e. localize where the hot-spots occur. To evaluate the localization performance of all the methods, we will compute the following four criteria: (1) precision, defined as the proportion of detected hot-spots that are true hot-spots; (2) recall, defined as the proportion of the hot-spots that are correctly identified; (3) F-measure, a single criterion that combines the precision and recall by calculating their harmonic mean. Moreover, we also compare the true positive rate (TPR), true negative rate (TNR), false positive rate (FPR), and false negative rate (FNR). The localization performance measuring in precision, recall, F-measure can be found in Tables 2 and 3, and the localization performance measuring in TPR, TNR, FPR, FNR can be found in Tables 4 and 5. For our proposed SSD-Tensor method, its localization performance is satisfactory no matter there is a stable or unstable global trend. For instance, when there is a decreasing global trend and
Predicting Chronic Homelessness: The Importance of Comparing Algorithms using Client Histories
Published in Journal of Technology in Human Services, 2022
Geoffrey Messier, Caleb John, Ayush Malik
The algorithms are compared using five classification metrics calculated when they are applied to the k-fold cross-validation testing data. True positive rate or sensitivity (Table 2 where the absolute number of true positive and false positives are provided in brackets.
Cost, healthcare utilization, and outcomes of antibody-mediated rejection in kidney transplant recipients in the US
Published in Journal of Medical Economics, 2021
Allyson Hart, David Zaun, Robbin Itzler, David Schladt, Ajay Israni, Bertram Kasiske
Several other algorithm options were explored. For example, ignoring the biopsy date increased the true-positive rate to 50%, but it also increased the false-positive rate to 21%. Limiting the window of time after the biopsy during which treatment codes were found reduced the true-positive rate from 40% to 37% using a 30-day window and 30% using a 10-day window, while only slightly reducing the false-positive rate from 4.1% to 3.4% for a 30-day and 2.3% for a 10-day window.