Results 271 to 280 of about 2,344,066 (336)
Some of the next articles are maybe not open access.
Semergen, 2023
The ROC curve is a statistical tool used to evaluate the discriminative capacity of a dichotomous diagnostic test. These are curves in which sensitivity is presented as a function of false positives (complementary to specificity) for different cut-off points.
J A, Martínez Pérez +1 more
openaire +1 more source
The ROC curve is a statistical tool used to evaluate the discriminative capacity of a dichotomous diagnostic test. These are curves in which sensitivity is presented as a function of false positives (complementary to specificity) for different cut-off points.
J A, Martínez Pérez +1 more
openaire +1 more source
ROC SURFACE: A GENERALIZATION OF ROC CURVE ANALYSIS
Journal of Biopharmaceutical Statistics, 2000Receiver operating characteristic (ROC) curve analysis is widely used in biomedical research to assess the performance of diagnostic tests. Much of the work has been directed at developing accurate indices to describe ROC curves and appropriate statistics to test differences between them.
H, Yang, D, Carlin
openaire +2 more sources
The ROC Curve Redefined - Optimizing Sensitivity (and Specificity) to the Lived Reality of Cancer.
New England Journal of Medicine, 2019The ROC Curve Redefined Though cancer’s setbacks didn’t necessarily threaten my life, they certainly threatened my days.
S. Walker
semanticscholar +1 more source
Journal of Computer-Aided Molecular Design, 2008
Two modifications to the standard use of receiver operating characteristic (ROC) curves for evaluating virtual screening methods are proposed. The first is to replace the linear plots usually used with semi-logarithmic ones (pROC plots), including when doing "area under the curve" (AUC) calculations.
Robert D, Clark, Daniel J, Webster-Clark
openaire +2 more sources
Two modifications to the standard use of receiver operating characteristic (ROC) curves for evaluating virtual screening methods are proposed. The first is to replace the linear plots usually used with semi-logarithmic ones (pROC plots), including when doing "area under the curve" (AUC) calculations.
Robert D, Clark, Daniel J, Webster-Clark
openaire +2 more sources
ROC Curves for Classification Trees
Medical Decision Making, 1994A common problem in medical diagnosis is to combine information from several tests or patient characteristics into a decision rule to distinguish diseased from healthy patients. Among the statistical procedures proposed to solve this problem, recursive partitioning is appealing for the easily-used and intuitive nature of the rules it produces.
R F, Raubertas +3 more
openaire +2 more sources
Medical Decision Making, 2000
The authors present a method to combine several independent studies of the same (continuous or semiquantitative) diagnostic test, where each study reports a complete ROC curve; a plot of the true-positive rate or sensitivity against the false-positive rate or one minus the specificity.
A D, Kester, F, Buntinx
openaire +2 more sources
The authors present a method to combine several independent studies of the same (continuous or semiquantitative) diagnostic test, where each study reports a complete ROC curve; a plot of the true-positive rate or sensitivity against the false-positive rate or one minus the specificity.
A D, Kester, F, Buntinx
openaire +2 more sources
2009 IEEE International Workshop on Machine Learning for Signal Processing, 2009
In classification problems, major methods focus on the minimization of the classification error rate. This is not always a suitable performance measure when sample numbers of classes are biased. In this case, the area under the Receiver Operating Characteristic curve (AUC) is an effective performance measure.
Takashi Takenouchi, Shinto Eguchi
openaire +1 more source
In classification problems, major methods focus on the minimization of the classification error rate. This is not always a suitable performance measure when sample numbers of classes are biased. In this case, the area under the Receiver Operating Characteristic curve (AUC) is an effective performance measure.
Takashi Takenouchi, Shinto Eguchi
openaire +1 more source
Receiver Operator Characteristic (ROC) Curves
Infection Control & Hospital Epidemiology, 1988The goal of diagnostic testing is to identify patients with a particular disease. Often, it is just as important that the test not mistakenly identify healthy persons as having disease. For example, a new test for the acquired immunodeficiency syndrome (AIDS) might identify 99.99% of all patients infected with the human ...
openaire +2 more sources
ROC Curves with Multiple Marker Measurements
Biometrics, 1995Properties of receiver operating characteristic (ROC) curves are explored for markers that are measured repeatedly, through space or time, for each subject. The true underlying response, positive or negative, of each subject is assumed to be constant across marker measurements, and is determined from assessment of some "gold standard." A marker-based ...
openaire +2 more sources
Transformed ROC Curve for Biomarker Evaluation
Statistics in MedicineABSTRACTTo complement the conventional area under the ROC curve (AUC) which cannot fully describe the diagnostic accuracy of some non‐standard biomarkers, we introduce a transformed ROC curve and its associated transformed AUC (TAUC) in this article, and show that TAUC can relate the original improper biomarker to a proper biomarker after a non ...
Jianping Yang +4 more
openaire +3 more sources