Results 261 to 270 of about 287,298 (306)

Understanding receiver operating characteristic (ROC) curves

CJEM, 2006
In this issue of the Journal, Auer and colleagues conclude that serum levels of neuron-specific enolase (NSE), a biochemical marker of ischemic brain injury, may have clinical utility for the prediction of survival to hospital discharge in patients experiencing the return of spontaneous circulation following at least 5 minutes of cardiopulmonary ...
Jerome, Fan, Suneel, Upadhye, Andrew, Worster   +2 more
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Comprehensive Analysis of Receiver Operating Characteristic (ROC) Curves for Hyperspectral Anomaly Detection

IEEE Transactions on Geoscience and Remote Sensing, 2022
Chein-I Chang
exaly   +2 more sources

Radiographic Applications of Receiver Operating Characteristic (ROC) Curves

Radiology, 1974
The basic concepts underlying the theory and experimental determination of receiver operating characteristic (ROC) curves are discussed. Such curves were used to describe the detectability of the image of 2 mm Lucite beads (similar to certain small gallstones) in a noisy background of radiographic mottle. Results are shown for four typical radiographic
D J, Goodenough, K, Rossmann, L B, Lusted   +2 more
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Receiver Operator Characteristic (ROC) Curves

Infection Control & Hospital Epidemiology, 1988
The 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 ...
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A test for crossing receiver operating characteristic (roc) curves

Communications in Statistics - Theory and Methods, 1988
The receiver operating characteristic (ROC) curve gives a graphical representation of sensitivity and specificity of a prediction model when varying the decision treshold on a diagnostic criterion. A classical test for comparing the overall accuracies for two models -1 and 2- is based on the difference between ROC curves areas - related to its standard
Alain Moise
exaly   +2 more sources

Statistical Approaches to the Analysis of Receiver Operating Characteristic (ROC) Curves

Medical Decision Making, 1984
In this article we review published and some unpublished work in statistical analyses of ROC curves. We describe both single and joint indices and indicate the approaches that have been taken to consider between-reader variations and correlations, within-reader variations, and variations and correlations between cases.
B J, McNeil, J A, Hanley
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Receiver Operating Characteristic (ROC) Curves: The Basics and Beyond

Hospital Pediatrics
Diagnostic tests and clinical prediction rules are frequently used to help estimate the probability of a disease or outcome. How well a test or rule distinguishes between disease or no disease (discrimination) can be measured by plotting a receiver operating characteristic (ROC) curve and calculating the area under it (AUROC).
Pearl W, Chang, Thomas B, Newman
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ROC-ing along: Evaluation and interpretation of receiver operating characteristic curves

Surgery, 2016
It is vital for clinicians to understand and interpret correctly medical statistics as used in clinical studies. In this review, we address current issues and focus on delivering a simple, yet comprehensive, explanation of common research methodology involving receiver operating characteristic (ROC) curves. ROC curves are used most commonly in medicine
Jane V, Carter, Jianmin, Pan, Shesh N, Rai, Susan, Galandiuk   +3 more
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Local linear smoothing of receiver operating characteristic (ROC) curves

Journal of Statistical Planning and Inference, 2004
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Peng, Liang, Zhou, Xiao-Hua
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ROC with confidence — a Perl program for receiver operator characteristic curves

Computer Methods and Programs in Biomedicine, 2001
Receiver operator characteristic (ROC) curves are recommended to assess the diagnostic value of tests depending on a single cut-off value of a continuous variable. These ROC curves show the true-positive rate (sensitivity) against the false-positive rate (1-specificity).
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