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Annals of Gastroenterological Surgery, EarlyView.ROC analyses of APRI score, EOB‐MRI, and a combination of EOB‐MRI and APRI score for blue liver. ABSTRACT Aim Sinusoidal obstruction syndrome (SOS), also known as “blue liver (BL),” is a common hepatic injury following oxaliplatin‐based chemotherapy in patients with colorectal liver metastases (CRLM).
Tomonari Shimagaki +7 more
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The area under the ROC curve as a measure of clustering quality
Data mining and knowledge discovery, 2020The area under the receiver operating characteristics (ROC) Curve, referred to as AUC, is a well-known performance measure in the supervised learning domain.
Pablo A. Jaskowiak +2 more
semanticscholar +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.
Harry Yang, David Carlin
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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
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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
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ROC curves and the binormal assumption
The Journal of Neuropsychiatry and Clinical Neurosciences, 1991Previous articles in this series have described how receiver operating characteristic (ROC) graphs provide comprehensive graphic representations of the diagnostic performance of non-binary tests and have explained how one constructs "trapezoidal" ROC graphs in which discrete cutoff points are plotted and connected with line segments.
Eugene Somoza, Douglas Mossman
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On the statistical analysis of ROC curves
Statistics in Medicine, 1989AbstractWe introduce a new accuracy index for receiver operating characteristic (ROC) curves, namely the partial area under the binormal ROC graph over any specified region of interest. We propose a simple but general procedure, based on a conventional analysis of variance, for comparing accuracy indices derived from two or more different modalities ...
Walter Zucchini, Mary Lou Thompson
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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.
Shinto Eguchi, Takashi Takenouchi
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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.
Shinto Eguchi, Takashi Takenouchi
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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
semanticscholar +1 more source
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.
Peter G. Szilagyi +3 more
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