The ROC curve to compare our new proposed criteria (blue line) with the old criteria (red line).

Receiver operating characteristic (ROC) curves and area under the curve (AUCs) for the different grading scales used in this studies, alone or in combination, with or without the factor “age” taken into account.

Frédéric Clarençon (4567939)
Éric Bardinet (4567927)
Jacques Martinerie (166019)
Vincent Pelbarg (4567942)
Nicolas Menjot de Champfleur (4567924)
Rajiv Gupta (309671)
Eléonore Tollard (4567930)
Gustavo Soto-Ares (4567936)
Danielle Ibarrola (327656)
Emmanuelle Schmitt (637540)
Thomas Tourdias (4339576)
Vincent Degos (621636)
Jérome Yelnik (4567933)
Didier Dormont (120582)
Louis Puybasset (69943)
Damien Galanaud (636954)

November 2017

Note that the AUC of the ROC curve of the DGM nuclei grading scale was higher than the one of the...

Receiver operating characteristics (ROC) curve for assessing goodness-of-fit for the top model selected from the candidate set.

Matthew L. Farnsworth (329830)
Ryan S. Miller (106337)
Kerri Pedersen (329831)
Mark W. Lutman (329832)
Seth R. Swafford (329833)
Philip D. Riggs (329834)
Colleen T. Webb (106335)

February 2013

The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...

Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.

Nadine Kraamwinkel (5018939)
Hans Ekbrand (6191126)
Stefania Davia (6191129)
Adel Daoud (3173796)

January 2019

Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...

ROC curve and the area under the curve (AUC) under different distributional assumptions for the BRCA data.

Mojtaba Ganjali (550736)
Taban Baghfalaki (667319)
Damon Berridge (729703)

April 2015

ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...

The ROC curve obtained from the composite dataset (n = 38) demonstrates an area under curve (AUC) of 0.80.

Hemant K. Roy (305615)
Charles B. Brendler (694853)
Hariharan Subramanian (292812)
Di Zhang (197959)
Charles Maneval (694854)
John Chandler (694855)
Leah Bowen (283348)
Karen L. Kaul (487905)
Brian T. Helfand (203249)
Chi-Hsiung Wang (694856)
Margo Quinn (694857)
Jacqueline Petkewicz (694858)
Michael Paterakos (694859)
Vadim Backman (305612)

February 2015

This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...

Comparison of various prediction methods in terms of the area under the ROC curve (AUC).

Bernd Lütkenhöner (486490)
Türker Basel (486491)

November 2013

Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p

Comparison of the performance of the models using area under the curve (AUC) of ROC.

Arjun Parthipan (6307895)
Imon Banerjee (6307898)
Keith Humphreys (28187)
Steven M. Asch (6307901)
Catherine Curtin (6307904)
Ian Carroll (415150)
Tina Hernandez-Boussard (4013)

February 2019

Comparison of the performance of the models using area under the curve (AUC) of ROC.</p

ROC curves for the different original algorithms.

Luca Navarini (4576774)
Domenico Paolo Emanuele Margiotta (4576768)
Francesco Caso (5848853)
Damiano Currado (5848856)
Marco Tasso (805939)
Silvia Angeletti (703420)
Massimo Ciccozzi (76730)
Raffaele Scarpa (3448187)
Antonella Afeltra (4576780)
Luisa Costa (5848859)

October 2018

Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....

ROC curves for different point cut-offs for the proposed survey tool.

Imad Cherkaoui (344820)
Radia Sabouni (127740)
Iraqi Ghali (546594)
Darya Kizub (546595)
Alexander C. Billioux (546596)
Kenza Bennani (546597)
Jamal Eddine Bourkadi (546598)
Abderrahmane Benmamoun (546599)
Ouafae Lahlou (127724)
Rajae El Aouad (127755)
Kelly E. Dooley (358104)

April 2014

The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...

ROC curves for the different algorithms adapted according to EULAR indications.

Luca Navarini (4576774)
Domenico Paolo Emanuele Margiotta (4576768)
Francesco Caso (5848853)
Damiano Currado (5848856)
Marco Tasso (805939)
Silvia Angeletti (703420)
Massimo Ciccozzi (76730)
Raffaele Scarpa (3448187)
Antonella Afeltra (4576780)
Luisa Costa (5848859)

October 2018

Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...

ROC curve and the area under the curve (AUC) under different distributional assumptions for the Golub data.

Mojtaba Ganjali (550736)
Taban Baghfalaki (667319)
Damon Berridge (729703)

April 2015

ROC curve and the area under the curve (AUC) under different distributional assumptions for the G...

ROC curve for the best classification models resulting from the LOO validation (ranking based on the AUC obtained for each classifier).

Diana de la Iglesia (649447)
Miguel García-Remesal (649448)
Alberto Anguita (649449)
Miguel Muñoz-Mármol (649450)
Casimir Kulikowski (649451)
Víctor Maojo (649452)

October 2014

ROC curve for the best classification models resulting from the LOO validation (ranking based on ...

ROC curves of model 1, A, consisting of symptoms.

Manon G. van der Meer (684116)
Barbra E. Backus (684117)
Yolanda van der Graaf (170406)
Maarten J. Cramer (684118)
Yolande Appelman (684119)
Pieter A. Doevendans (137859)
A. Jacob Six (684120)
Hendrik M. Nathoe (398187)

January 2015

The black line describes the diagnostic value in men and the red line the diagnostic value in wom...

A plot to show the different ROC curves obtained by the 10-fold cross-validation under different values.

Yan Xu (14594)
Xin Wen (470639)
Li-Shu Wen (615350)
Ling-Yun Wu (21548)
Nai-Yang Deng (615351)
Kuo-Chen Chou (117713)

August 2014

As we can see, when , the corresponding AUC (i.e., the area under its curve) is the largest, mean...

ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation cohort by different cancer stages.

Yung-Heng Lee (6204656)
Pei-Tseng Kung (593657)
Yueh-Hsin Wang (593658)
Wei-Yin Kuo (5439602)
Su-Ling Kao (6204659)
Wen-Chen Tsai (593659)

January 2019

ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation c...

Receiver operating characteristic (ROC) curves and area under the curve (AUCs) for the different grading scales used in this studies, alone or in combination, with or without the factor “age” taken into account.

Frédéric Clarençon (4567939)
Éric Bardinet (4567927)
Jacques Martinerie (166019)
Vincent Pelbarg (4567942)
Nicolas Menjot de Champfleur (4567924)
Rajiv Gupta (309671)
Eléonore Tollard (4567930)
Gustavo Soto-Ares (4567936)
Danielle Ibarrola (327656)
Emmanuelle Schmitt (637540)
Thomas Tourdias (4339576)
Vincent Degos (621636)
Jérome Yelnik (4567933)
Didier Dormont (120582)
Louis Puybasset (69943)
Damien Galanaud (636954)

November 2017

Note that the AUC of the ROC curve of the DGM nuclei grading scale was higher than the one of the...

Receiver operating characteristics (ROC) curve for assessing goodness-of-fit for the top model selected from the candidate set.

Matthew L. Farnsworth (329830)
Ryan S. Miller (106337)
Kerri Pedersen (329831)
Mark W. Lutman (329832)
Seth R. Swafford (329833)
Philip D. Riggs (329834)
Colleen T. Webb (106335)

February 2013

The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...

Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.

Nadine Kraamwinkel (5018939)
Hans Ekbrand (6191126)
Stefania Davia (6191129)
Adel Daoud (3173796)

January 2019

Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...

ROC curve and the area under the curve (AUC) under different distributional assumptions for the BRCA data.

Mojtaba Ganjali (550736)
Taban Baghfalaki (667319)
Damon Berridge (729703)

April 2015

ROC curve and the area under the curve (AUC) under different distributional assumptions for the B...

The ROC curve obtained from the composite dataset (n = 38) demonstrates an area under curve (AUC) of 0.80.

Hemant K. Roy (305615)
Charles B. Brendler (694853)
Hariharan Subramanian (292812)
Di Zhang (197959)
Charles Maneval (694854)
John Chandler (694855)
Leah Bowen (283348)
Karen L. Kaul (487905)
Brian T. Helfand (203249)
Chi-Hsiung Wang (694856)
Margo Quinn (694857)
Jacqueline Petkewicz (694858)
Michael Paterakos (694859)
Vadim Backman (305612)

February 2015

This translated into a sensitivity = 88% and specificity = 72% for discriminating between progres...

Comparison of various prediction methods in terms of the area under the ROC curve (AUC).

Bernd Lütkenhöner (486490)
Türker Basel (486491)

November 2013

Comparison of various prediction methods in terms of the area under the ROC curve (AUC).</p

Comparison of the performance of the models using area under the curve (AUC) of ROC.

Arjun Parthipan (6307895)
Imon Banerjee (6307898)
Keith Humphreys (28187)
Steven M. Asch (6307901)
Catherine Curtin (6307904)
Ian Carroll (415150)
Tina Hernandez-Boussard (4013)

February 2019

Comparison of the performance of the models using area under the curve (AUC) of ROC.</p

ROC curves for the different original algorithms.

Luca Navarini (4576774)
Domenico Paolo Emanuele Margiotta (4576768)
Francesco Caso (5848853)
Damiano Currado (5848856)
Marco Tasso (805939)
Silvia Angeletti (703420)
Massimo Ciccozzi (76730)
Raffaele Scarpa (3448187)
Antonella Afeltra (4576780)
Luisa Costa (5848859)

October 2018

Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.864 (95% CI 0....

ROC curves for different point cut-offs for the proposed survey tool.

Imad Cherkaoui (344820)
Radia Sabouni (127740)
Iraqi Ghali (546594)
Darya Kizub (546595)
Alexander C. Billioux (546596)
Kenza Bennani (546597)
Jamal Eddine Bourkadi (546598)
Abderrahmane Benmamoun (546599)
Ouafae Lahlou (127724)
Rajae El Aouad (127755)
Kelly E. Dooley (358104)

April 2014

The area under the ROC curve (AUC) for each score and its 95% confidence interval are provided.</...

ROC curves for the different algorithms adapted according to EULAR indications.

Luca Navarini (4576774)
Domenico Paolo Emanuele Margiotta (4576768)
Francesco Caso (5848853)
Damiano Currado (5848856)
Marco Tasso (805939)
Silvia Angeletti (703420)
Massimo Ciccozzi (76730)
Raffaele Scarpa (3448187)
Antonella Afeltra (4576780)
Luisa Costa (5848859)

October 2018

Areas under the curve (AUC)-values (95% CI) are 0.7679 (95% CI 0.64768 to 0.88812), 0.8648 (95% CI 0...

ROC curve and the area under the curve (AUC) under different distributional assumptions for the Golub data.

Mojtaba Ganjali (550736)
Taban Baghfalaki (667319)
Damon Berridge (729703)

April 2015

ROC curve and the area under the curve (AUC) under different distributional assumptions for the G...

ROC curve for the best classification models resulting from the LOO validation (ranking based on the AUC obtained for each classifier).

Diana de la Iglesia (649447)
Miguel García-Remesal (649448)
Alberto Anguita (649449)
Miguel Muñoz-Mármol (649450)
Casimir Kulikowski (649451)
Víctor Maojo (649452)

October 2014

ROC curve for the best classification models resulting from the LOO validation (ranking based on ...

ROC curves of model 1, A, consisting of symptoms.

Manon G. van der Meer (684116)
Barbra E. Backus (684117)
Yolanda van der Graaf (170406)
Maarten J. Cramer (684118)
Yolande Appelman (684119)
Pieter A. Doevendans (137859)
A. Jacob Six (684120)
Hendrik M. Nathoe (398187)

January 2015

The black line describes the diagnostic value in men and the red line the diagnostic value in wom...

A plot to show the different ROC curves obtained by the 10-fold cross-validation under different values.

Yan Xu (14594)
Xin Wen (470639)
Li-Shu Wen (615350)
Ling-Yun Wu (21548)
Nai-Yang Deng (615351)
Kuo-Chen Chou (117713)

August 2014

As we can see, when , the corresponding AUC (i.e., the area under its curve) is the largest, mean...

ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation cohort by different cancer stages.

Yung-Heng Lee (6204656)
Pei-Tseng Kung (593657)
Yueh-Hsin Wang (593658)
Wei-Yin Kuo (5439602)
Su-Ling Kao (6204659)
Wen-Chen Tsai (593659)

January 2019

ROC curve (receiver operating characteristic curve) and area under curves (AUCs) of the validation c...

Receiver operating characteristic (ROC) curves and area under the curve (AUCs) for the different grading scales used in this studies, alone or in combination, with or without the factor “age” taken into account.

Frédéric Clarençon (4567939)
Éric Bardinet (4567927)
Jacques Martinerie (166019)
Vincent Pelbarg (4567942)
Nicolas Menjot de Champfleur (4567924)
Rajiv Gupta (309671)
Eléonore Tollard (4567930)
Gustavo Soto-Ares (4567936)
Danielle Ibarrola (327656)
Emmanuelle Schmitt (637540)
Thomas Tourdias (4339576)
Vincent Degos (621636)
Jérome Yelnik (4567933)
Didier Dormont (120582)
Louis Puybasset (69943)
Damien Galanaud (636954)

November 2017

Note that the AUC of the ROC curve of the DGM nuclei grading scale was higher than the one of the...

Receiver operating characteristics (ROC) curve for assessing goodness-of-fit for the top model selected from the candidate set.

Matthew L. Farnsworth (329830)
Ryan S. Miller (106337)
Kerri Pedersen (329831)
Mark W. Lutman (329832)
Seth R. Swafford (329833)
Philip D. Riggs (329834)
Colleen T. Webb (106335)

February 2013

The area under the curve (AUC) is 0.76, suggesting a strong ability to discriminate between true ...

Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.

Nadine Kraamwinkel (5018939)
Hans Ekbrand (6191126)
Stefania Davia (6191129)
Adel Daoud (3173796)

January 2019

Area under the ROC curve sensitivity = 77.5%, specificity = 61.3%, AUC = 0.751 with cutoff = 0.488.<...

The ROC curve to compare our new proposed criteria (blue line) with the old criteria (red line).

Abstract

Extracted data

The ROC curve to compare our new proposed criteria (blue line) with the old criteria (red line).

Abstract

Extracted data

Related items

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