Results 281 to 290 of about 3,583,799 (330)
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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
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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. The result of the analysis is a pooled ROC curve, with a confidence
A D, Kester, F, Buntinx
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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. The result of the analysis is a pooled ROC curve, with a confidence
A D, Kester, F, Buntinx
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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
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On the Shape of the Population ROC Curve
Academic Radiology, 2013Human observers often do not produce empirical operating points near the northeast corner of the receiver operating characteristic (ROC) plot, and thus the local shape of the population ROC curve is unknown.We call attention to occult abnormalities and propose that considerations by human observers of the prior probability of occult abnormalities can ...
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Estimation of a convex ROC curve
Statistics & Probability Letters, 2002zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Pattern Recognition Letters, 2017
This paper shows that ROC curves that are constructed with nonrandom data are biased.The magnitude of this bias is explored using simulations.A procedure for plotting consistent ROC curves is introduced.The presented procedure works well with simulated and non-simulated data. This paper shows that when a classifier is evaluated with nonrandom test data,
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This paper shows that ROC curves that are constructed with nonrandom data are biased.The magnitude of this bias is explored using simulations.A procedure for plotting consistent ROC curves is introduced.The presented procedure works well with simulated and non-simulated data. This paper shows that when a classifier is evaluated with nonrandom test data,
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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 ...
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Regression Models for Convex ROC Curves
Biometrics, 2000Summary. The performance of a diagnostic test is summarized by its receiver operating characteristic (ROC) curve. Under quite natural assumptions about the latent variable underlying the test, the ROC curve is convex. Empirical data on a test's performance often comes in the form of observed true positive and false positive relative frequencies under ...
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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
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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
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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
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