ライフサイエンスコーパス: ROC curve

1 sepsis from severe SIRS (0.742-0.917 AUC of ROC curves).

2 models tested yielded ~72% of area under the ROC curve.

3 inciple produce 50 positive instances in the ROC curves.

4 ectiveness to discriminate both groups using ROC curves.

5 imized by receiver operating characteristic (ROC) curve.

6 by using a receiver operator characteristic (ROC) curve.

7 es with a receiver operating characteristic (ROC) curve.

8 ulated by receiver operating characteristic (ROC) curve.

9 under the receiver operating characteristic (ROC) curve.

10 under the receiver operating characteristic (ROC) curve.

11 ted using receiver operating characteristic (ROC) curves.

12 ate model receiver operating characteristic (ROC) curves.

13 under the receiver operating characteristic (ROC) curves.

14 red using receiver operating characteristic (ROC) curves.

15 tion uses receiver operating characteristic (ROC) curves.

16 ned using receiver operating characteristic (ROC) curves.

17 nted with receiver operating characteristic (ROC) curves.

18 (AUC) of receiver operating characteristic (ROC) curves.

19 he use of receiver operating characteristic (ROC) curves.

20 sessed by receiver operating characteristic (ROC) curves.

21 ted using Receiver Operating Characteristic (ROC) curves.

22 ulated by receiver operating characteristic (ROC) curves.

23 luated by receiver operating characteristic (ROC) curves.

24 ved using receiver operative characteristic (ROC) curves.

25 ILI after acetaminophen overdose (area under ROC curve 0.98 (95% CI; 0.96-1), P < 0.0001).

26 under the receiver operating characteristic (ROC) curve 0.78 and 0.80, respectively).

27 racy than CAP in terms of the area under the ROC curve (0.99 vs 0.77, respectively; P = .0334).

28 ncer decreased after compression (area under ROC curve = 0.53 vs 0.71, respectively; P = .02).

29 lidated test set performance: area under the ROC curve = 0.767 (descriptors only: 0.736/background pr

30 and high diagnostic accuracy (area under the ROC curve = 0.95; 95% confidence interval [CI]: 0.93, 0.

31 igher in AD compared to controls, area under ROC curve = 0.96) was identified as a fragment of pleiot

32 73 m(2) at 1 year [receiver operating curve (ROC curve), 0.78, 95% CI, 0.68-0.89].

33 rimination of dCON (P < .001; area under the ROC curve, 0.66).

34 dicted subsequent grade 3-4 GVHD (area under ROC curve, 0.76).

35 morphologic assessment alone (area under the ROC curve, 0.767 vs 0.487 [P = .00005] and 0.802 vs 0.48

36 velopment of peak grade 3-4 GVHD (area under ROC curve, 0.88).

37 hin 1 year after transplantation (area under ROC curve, 0.90).

38 om immunocompetent patients (areas under the ROC curve, 0.91 and 0.97).

39 n the one for RV volume (mean area under the ROC curve, 0.96 +/- 0.02 vs 0.88 +/- 0.04; P = .009).

40 f intra-abdominal infection (areas under the ROC curve: 0.775 vs 0.689, respectively, P = 0.03).

41 he fourth postoperative day (areas under the ROC curve: 0.783 vs 0.671, P = 0.0002).

42 city based on optimal operating point on the ROC curve (7.0 ng/mL) were both 93%.

43 9.9% (95% CI 53.7%-75.5%) and area under the ROC curve= 76.0% (95% CI 56.8%-82.1%).

44 ms, the Model 2 showed higher area under the ROC curve (82.2%, 95% CI 79.6%-84.7%) and good calibrati

45 The MUAC area under the ROC curve accuracy level in identifying severe wasting w

46 ate-of-the-art methods, with areas under the ROC curve all over 0.8 for four types of cancer.

47 ROC curve analyses demonstrated 60-100% sensitivity and

48 ver, the receiver operating characteristics (ROC) curve analyses of miR-630 and miR-378g yielded area

49 Paired-receiver operating characteristic (ROC) curve analyses revealed no statistical differences

50 Receiver operating characteristic (ROC) curve analyses revealed that nDNA fragments, GDH, a

51 ROC curve analysis at 120 days after ingestion showed th

52 ROC curve analysis determined an ADC value of 958 x 10(-

53 The ROC curve analysis identified an optimal cutoff value of

54 ROC curve analysis indicated that the optimal cutoff poi

55 ROC curve analysis of an independent, more heterogeneous

56 ylaxis and cardiovascular/febrile reactions, ROC curve analysis revealed a reasonably high area under

57 ROC curve analysis revealed the current impedance standa

58 A ROC curve analysis was performed in the derivation cohor

59 e of the strain ratio (4.25) was obtained by ROC curve analysis, the sensitivity and specificity for

60 tive and False Positive trade-off, through a ROC curve analysis.

61 Cut-off values were established by applying ROC curve analysis.

62 FTND and NCC in discerning disease severity (ROC curve analysis: AUC = 0.746, P = 0.027) was superior

63 ted using Receiver-Operating Characteristic (ROC) curve analysis (AUC) and its 95 % Confidence Interv

64 red using receiver-operating-characteristic (ROC) curve analysis and Kaplan-Meier survival curves.

65 included receiver operating characteristic (ROC) curve analysis and noninferiority analysis.

66 Receiver operating characteristic (ROC) curve analysis demonstrated a CT-score cut-off of 1

67 The receiver operator characteristic (ROC) curve analysis demonstrated that the CI candidacy e

68 ated with receiver operating characteristic (ROC) curve analysis for each method, with stress MBF and

69 e PCR and receiver operating characteristic (ROC) curve analysis in five cohorts (n = 345) from the N

70 BF) data, receiver operating characteristic (ROC) curve analysis of the posterior cingulate cortex (P

71 Receiver operating characteristic (ROC) curve analysis resulted in area under the ROC curve

72 r, receiver operating characteristic curves (ROC) curve analysis revealed a significant area under cu

73 Receiver operating characteristic (ROC) curve analysis showed excellent predictive accuracy

74 Receiver operator characteristic (ROC) curve analysis showed that IL17A can be used to dis

75 Receiver operation characteristic (ROC) curve analysis showed that the trifucosylated N-gly

76 Receiver operating characteristic (ROC) curve analysis was carried out on diagnostic decisi

77 Receiver operating characteristic (ROC) curve analysis was performed to define cut-off valu

78 Receiver operating characteristic (ROC) curve analysis was performed to evaluate the discri

79 Receiver operator characteristic (ROC) curve analysis was used to assess the diagnostic po

80 Using receiver operating characteristic (ROC) curve analysis, optimal cut-off values for left ven

81 ng by the receiver operating characteristic (ROC) curve analysis.

82 d through receiver operating characteristic (ROC) curve analysis.

83 based on receiver operating characteristic (ROC) curve analysis.

84 ation and receiver operating characteristic (ROC) curve analysis.

85 sessed by receiver-operating-characteristic (ROC) curve analysis.

86 ined with receiver operating characteristic (ROC) curve analysis.

87 Receiver operating characteristic (ROC) curves analysis and Area Under the Curve (AUC) esti

88 Using ROC-curve analysis, serum HCV-RNA cut-offs for ruling in

89 Using ROC-curve analysis, serum HCV-RNA cutoffs for ruling in/

90 he receiver operating characteristic curves (ROC curves) analysis for evaluation of clinical diagnosi

91 using the receiver operating characteristic (ROC) curve and compared using the DeLong test.

92 under the receiver operating characteristic (ROC) curve and the sensitivity with fixed specificities

93 using the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics.

94 Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calc

95 parison of receiver-operator characteristic (ROC) curves and Decision Curve Analysis (DCA).

96 under the receiver operator characteristic (ROC) curves and multivariate generalized additive model

97 sessed by receiver operating characteristic (ROC) curves and survival analyses.

98 rithm with receiver operator characteristic (ROC) curves and the weighted kappa statistic.

99 nerated a receiver operating characteristic (ROC) curve, and calculated area under the curve (AUC) an

100 modeling, receiver-operator characteristic (ROC) curves, and calibration plots.

101 inal nerve fiber layer predictions showed an ROC curve area of 0.86 (95% CI, 0.83-0.88) to discrimina

102 gressors (slope faster than 2 mum/year), the ROC curve area was 0.96 (95% CI, 0.94-0.98), with a sens

103 Receiver operating characteristic (ROC) curves, area under the curve (AUC), and thresholds

104 response (receiver operating characteristic [ROC] curve, area under the curve [AUC] = 0.82, P = 0.02)

105 sing their Receiver Operator Characteristic (ROC) curves; Area Under Curve (AUC) and accuracy were ca

106 In the training period, the area under the ROC curve (aROC) of airborne particle counting and EBC w

107 AUCs) on receiver operating characteristics (ROC) curves as performance measures, the models did comp

108 The prognostic score, with ROC curve AUC at baseline of 0.753 (95% CI 0.723-0.781)

109 from healthy controls (global area under the ROC curve (AUC) 0.90 [95% CI 0.85-0.95]), latent tubercu

110 -fold cross validation, using area under the ROC curve (AUC) and Akaike Information Criterion (AIC) m

111 (ROC) analysis to generate an area under the ROC curve (AUC) and sensitivity and specificity distribu

112 stic (ROC) analysis, with the area under the ROC curve (AUC) as a figure of merit in the task of dist

113 The largest area under the ROC curve (AUC) for any single parameter was 0.75.

114 ility, and approximately 0.89 area under the ROC curve (AUC) for disorder prediction.

115 The primary outcome was the area under the ROC curve (AUC) for GFAP in patients with CT-negative an

116 ur classifiers achieved 72.2% Area Under the ROC Curve (AUC) for predicting carcinogenicity and 82.3%

117 The highest area under the ROC curve (AUC) for steatosis of PNPLA3 rs738409 genotyp

118 The area under the ROC curve (AUC) for the assessment of group 1 was 0.78 (

119 The area under the ROC curve (AUC) in the differentiation of stage F2 fibro

120 The area under the ROC curve (AUC) is often used to evaluate the performanc

121 reached an optimism-corrected area under the ROC curve (AUC) of 0.86.

122 n, DDRGIP method achieves the area under the ROC curve (AUC) of 0.9600 and 0.9636 which are better th

123 (ROC) was used to compare the area under the ROC curve (AUC) of each index.

124 FM framework, we compared the area under the ROC curve (AUC) of models developed with TRL-FM and othe

125 formance was scored using the Area Under the ROC Curve (AUC) statistic.

126 in provinces of the PRC, with area under the ROC curve (AUC) values of 0.75-0.76.

127 The highest area under the ROC curve (AUC) values were obtained at 200 Hz and range

128 The area under the ROC curve (AUC) was highest for the 100%-count, 60-min i

129 Area under ROC curve (AUC) was used as diagnostic performance crite

130 aracteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifi

131 mance was evaluated using the area under the ROC curve (AUC), the Enrichment Factor (EF) and Hit Rate

132 ach to train it by maximizing area under the ROC curve (AUC), which is an unbiased measure for class-

133 Under the receiver operating characteristic (ROC) Curve (AUC) can be improved from around 0.60 using

134 under the receiver operating characteristic (ROC) curve (AUC) of 0.81, while EMR data alone achieved

135 under the receiver operating characteristic (ROC) curve (AUC) of 1.00, 0.75 and 0.73, respectively.

136 under the receiver operating characteristic (ROC) curve (AUC) was calculated for transverse shear-wav

137 under the receiver operating characteristic (ROC) curve (AUC), number of false discoveries and statis

138 under the receiver-operator characteristic (ROC) curve (AUC).

139 under the receiver operating characteristic (ROC) curve (AUC); the Mean Reciprocal Ranking (MRR) of p

140 rea under receiver operating characteristic (ROC) curves (AUC) and sensitivities at fixed specificiti

141 ivity, 80%; specificity, 91%; area under the ROC curve [AUC] = 0.937; P = .0001), areas of lowest sig

142 The accuracies of ECVDEP (area under the ROC curve [AUC], 0.85) and normalized ECVDEP (AUC, 0.86)

143 set and generated the boxplot, accuracy, and ROC curve, AUC from each trained model with the test dat

144 The accuracy and area under the ROC curve, AUC, was 88.9% and 0.94, respectively.

145 e T1 and T2 yielded the best areas under the ROC curve (AUCs) of 0.975 and 0.979, respectively, for d

146 sequence classifiers yielded areas under the ROC curves (AUCs) [95% confidence intervals] of AUC(DCE)

147 Pairwise comparisons of areas under ROC curves (AUCs) from the different grading systems wer

148 better predictive abilities [area under the ROC curves (AUCs) of 0.67 and 0.69] than did weight-for-

149 under the receiver operating characteristic (ROC) curves (AUCs) on the test set were 0.88 (95% confid

150 eas under receiver-operating characteristic (ROC) curves (AUCs) were used to evaluate their predictiv

151 ROC curve (AUROC) analyses compared the diagnostic accur

152 e combined score exhibited an area under the ROC curve (AUROC) of 0.81 for discriminating future deme

153 osis grade 0-1 vs 2-3 with an area under the ROC curve (AUROC) of 0.95 (95% CI, 0.91-0.98), and grade

154 The area under the ROC curve (AUROC) of TE and HVPG for prediction of LREs

155 The area under the ROC curve (AUROC) was 0.98 for peanut, 0.97 for cashew,

156 under the receiver operating characteristic [ROC] curve [AUROC] for wPRx was 0.73 versus 0.66 for PRx

157 under the receiver operator characteristic (ROC) curves (Az value), specificity, sensitivity, positi

158 assification (optimal cut point, 1.55 SUVR), ROC curves based on clinical classification (optimal cut

159 d elderly versus Alzheimer disease dementia, ROC curves based on visual Abeta-positive/Abeta-negative

160 served good convergence among the 4 methods: ROC curves based on visual classification (optimal cut p

161 (SUVRs), receiver-operating-characteristic (ROC) curves based on clinical classification of cognitiv

162 no significant difference in area under the ROC curve between detector-reconstruction combinations a

163 arker was more predictive than another using ROC curves, but multiple logistic regression suggested s

164 The ROC curve calculation provided the following optimum dia

165 ion using receiver operating characteristic (ROC) curves, calibration using Cox linear logistic regre

166 Receiver operating characteristic (ROC) curves, category-free net reclassification index (c

167 A receiver-operating characteristics (ROC) curve combining both lipid levels predicted EAD wit

168 n that of receiver-operating-characteristic (ROC) curves, confirming similar findings in other class-

169 ROC curves constructed to determine optimal size and att

170 The ROC curve decided that the cutoff point of FENO was 37.8

171 62 K. pneumoniae and 348 E. faecium samples, ROC curves demonstrate that the conserved-sequence genom

172 Summary ROC curve demonstrated overall superiority of 3-T imagin

173 ROC curve demonstrated that studies receiving greater th

174 Summary receiver operator characteristic (ROC) curve demonstrated superior accuracy of direct MR a

175 arison of receiver operating characteristic (ROC) curves demonstrated that HAS-BLED had the best disc

176 ased on a Receiver operating characteristic (ROC) curve - derived optimum cut-off level of >140mAU/ml

177 ROC curves displayed an AUC higher than 0.8 and simulati

178 The model yielded an area under the ROC curve exceeding 0.81 in an independent testing set,

179 DNI demonstrated the highest area under the ROC curve for diagnosis of VUR (0.620, 95% CI 0.542-0.69

180 The test set area under the ROC curve for predicting EGFR mutation status was 0.89.

181 without IFI (P = 0.618); the area under the ROC curve for predicting IFIs was 0.56 (poor test).

182 nstrated significantly higher area under the ROC curve for protocol B (P < .0022), with interreader a

183 The ROC curve for ROPtool's tortuosity assessment had an are

184 The AUC ROC curve for the detection of IBI for the PCT assay was

185 e without IA (P = 0.098); the area under the ROC curve for the diagnosis of IA was 0.77 (fair test, i

186 Areas under the ROC curve for the full models were 0.69 (95% CI = 0.67 -

187 Results The average area under the ROC curve for the groups increased with experience (0.94

188 The area under the ROC curve for the model and validation subsets of the di

189 The global ROC curve for the Offsides ADE candidates ranked with th

190 The area under the ROC curves for discriminating glaucomatous from healthy

191 The area under the ROC curves for OD/LREs was 0.648 and 0.742 for LB and FI

192 under the receiver operating characteristic (ROC) curve for case-control discrimination based on firs

193 a under a receiver-operating characteristic (ROC) curve for each analyte were used to determine assoc

194 C) of the receiver operating characteristic (ROC) curve for performance evaluation.

195 under the receiver operating characteristic (ROC) curve for the classifiers was 0.973 with a sensitiv

196 generated receiver-operator characteristic (ROC) curves for the full models overlaid with Index as a

197 ly BMOBRW parameter that achieved area under ROC curve >=0.80 was infero-temporal (0.82).

198 e RNFLT parameters that achieved areas under ROC curve >=0.80 were global (0.89), supero-temporal (0.

199 outcome (p = 0.009), while the corresponding ROC curve had AUC of 0.784 and 0.783.

200 terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model sim

201 According to ROC curves, in the tumor group, at the cut-off value of

202 ROC curves indicated that baseline IgE against peanut an

203 ing best as determined by the area under the ROC curve (Mean AUC = 0.722).

204 Using pCR as the reference standard and ROC curve methodology, %TOITN AUC was 0.60 (95% CI, 0.39

205 r detection, with the highest area under the ROC curve obtained for LWF (0.97 in the peripheral zone

206 Furthermore, the area under the ROC curve obtained was 0.93, demonstrating that the prob

207 tes alone, which exhibited an area under the ROC curve of 0.57 (p < 0.008).

208 C) curve analysis resulted in area under the ROC curve of 0.676 (95% confidence interval: 0.58, 0.77)

209 rence prediction model had an area under the ROC curve of 0.786.

210 and atypical bacteria with an area under the ROC curve of 0.79 (95% CI, .75-.82).

211 y of 88.1% in our dataset with an AUC at the ROC curve of 0.85, whereas the accuracy values in the da

212 ns) can identify SPEs with an area under the ROC curve of 0.89.

213 ROC analysis revealed an area under the ROC curve of 0.893 for FAcontra and of 0.815 for FAint,

214 del classifies images with an area under the ROC curve of 0.897, and a sensitivity of 0.783 and speci

215 tortuosity assessment had an area under the ROC curve of 0.917.

216 The DL algorithm had an area under the ROC curve of 0.979 (95% CI: 0.974 to 0.984) and an overa

217 esults The algorithm achieved area under the ROC curve of 0.98 (95% confidence interval: 0.94, 1.00)

218 omarker for MDD, achieving an area under the ROC curve of 0.999 in discriminating drug-naive MDD pati

219 miR31 and FEM1C presented an area under the ROC curve of 96.7%, followed by SENP1 with 93.3%.

220 The ROC curve of hypoxanthine returned an AUC of 0.79 (p < 0

221 The area under the ROC curve of the algorithm for each corneal disease type

222 miR-630 and miR-378g yielded areas under the ROC curves of 0.82 (95% CI 0.67 to 0.82) and 0.83 (95% C

223 ROC curves of Cho SNR showed statistically significant d

224 under the receiver operating characteristic (ROC) curve of 0.73 (95% confidence interval [CI], .69-.7

225 under the receiver operating characteristic (ROC) curve of 0.75 +/- 0.1, 0.80 +/- 0.1 and 0.89 +/- 0.

226 under the receiver operating characteristic (ROC) curve of 0.8 compared to 0.73 with the AHP based mo

227 under the receiver operator characteristic (ROC) curve of 0.8.

228 under the receiver operating characteristic (ROC) curve of 0.911 (95% CI 0.81 to 0.98).

229 under the receiver operating characteristic (ROC) curve of 0.93 on 10-fold cross-validation.

230 Receiver operating characteristic (ROC) curves of the predictive factors were used to ident

231 rage Specificity (MAS), a scalar measure for ROC curve, of 0.97 (0.96).

232 ound between their respective area under the ROC curve (p=0.0650).

233 nt difference within the methods between the ROC curves (P > 0.4) for progression-free survival and o

234 LS mimics (difference between area under the ROC curves: p=0.0001 and p=0.0005; respectively).

235 ation and receiver operating characteristic (ROC) curves produced.

236 te urinary tract obstruction (area under the ROC curve ranged from 0.50 to 0.62).

237 The GRS results in an area under the ROC curve ranging between 0.64 and 0.72, within European

238 on set reveal high accuracy with areas under ROC curve ranging from 0.842 to 0.894.

239 under the receiver operating characteristic (ROC) curve, relative to the other five methods.

240 For comparison with current practice, ROC curves relying solely on percent weight loss were al

241 y identified markers were evaluated applying ROC curves resulting in individual marker AUC >90% both

242 The receiver operating characteristics (ROC) curve revealed a mean area under the curve (AUC) of

243 The AUC statistic quantified the ROC curve's capacity to classify participants likely to

244 The area under the curve (AUC) for each ROC curve serves as a quantitative metric to optimize tw

245 ROC curve showed that miR-125a-3p abundant level may pre

246 The ROC curve showed the optimum cut-off point at 0.628 x 10

247 ROC curves showed poor diagnostic performance of median

248 Analysis of ROC curves showed that high-intensity long-run emphasis

249 say offered an area under the curve (AUC) of ROC curve similar to that for CRP concentration for the

250 LLM accuracy was assessed by summary ROC curve (sROC) analysis and estimated by the area unde

251 According to the ROC curve, the learning curve was completed after 25 pro

252 On the basis of ROC curves, the most discriminative cutoff value for MTV

253 ROC curve: the SI cutoff was 0.52 and 0.33 respectively

254 chniques were used to compare groups and the ROC curve to evaluate classification algorithms.

255 ROC curves to determine the optimal viral load cutoff pr

256 ploys the receiver operating characteristic (ROC) curve to minimize false discovery rate (FDR) and ca

257 te serial receiver operator characteristics (ROC) curves to assess the sensitivity and specificity of

258 We used receiver operating characteristic (ROC) curves to determine the ability of LB and FIB-4 to

259 We used receiver operating characteristic (ROC) curves to determine the ability of TE and HVPG to p

260 generated receiver operating characteristic (ROC) curves to evaluate the optimal ZD cutoff criteria.

261 We used receiver operating characteristic (ROC) curves to examine the prognostic accuracy of the in

262 We used receiver operating characteristic (ROC) curves to present balance in sensitivity and specif

263 The area under the ROC curve using these significant VOCs to discriminate e

264 Finally, miR95 had an area under the ROC curve value <86.7%.

265 racy was 64% (16/25), and the area under the ROC curve was 0.601 (95% confidence interval [95% CI], 0

266 The area under the ROC curve was 0.91 (0.80 with 4-fold cross-validation, P

267 (23/25) (P < 0.001), and the area under the ROC curve was 0.929 (95% CI, 0.740-0.990).

268 AUC of the ROC curve was 0.999.

269 (25/25) (P < 0.001), and the area under the ROC curve was 1 (95% CI, 0.863-1).

270 The area under the ROC curve was calculated for the ROPtool, and sensitivit

271 ariables in a given model and the area under ROC curve was calculated.

272 The ROC curve was drawn to differentiate grade II from grade

273 The ROC curve was significant for SEVs concentration to pred

274 Area under the ROC curve was significantly higher (P = .002) for ADC (0

275 under the receiver operating characteristic (ROC) curve was 0.95.

276 under the receiver operating characteristic (ROC) curve was 0.97 in the de novo cross-validation when

277 A receiver operating characteristic (ROC) curve was calculated, showing the sensitivity and s

278 A receiver operating characteristic (ROC) curve was carried out to identify the cutoff for co

279 Receiver operating characteristic (ROC) curve was drawn to determine a cut-off ADC value fo

280 Using ROC curve, we calculated the area under the curve (AUC)

281 Using receiver operating characteristic (ROC) curves, we determined the optimal cut-off values fo

282 The areas under the ROC curves were 0.89 (95% CI: 0.84-0.95) for global RNFL

283 Sensitivity, specificity, and ROC curves were compared for both sexes.

284 nder the receiver operative characteristics (ROC) curves were 1.0, which represents a highly sensitiv

285 Receiver operating characteristic (ROC) curves were analyzed by evaluating the area under t

286 Receiver operating characteristic (ROC) curves were constructed to assess the performance o

287 Receiver operating characteristic (ROC) curves were constructed to describe the ability of

288 Receiver operating characteristic (ROC) curves were constructed to determine optimal discri

289 Receiver operating characteristic (ROC) curves were constructed to determine the optimal th

290 Receiver operating characteristic (ROC) curves were constructed to estimate the ability of

291 Receiver operating characteristic (ROC) curves were created.

292 Receiver operating characteristic (ROC) curves were generated to determine area under the c

293 Receiver Operating Characteristic (ROC) curves were obtained.

294 of TMJOA, receiver operating characteristic (ROC) curves were plotted.

295 Receiver-operating characteristic (ROC) curves were then calculated to determine the optimu

296 quisition, receiver operator characteristic (ROC) curves were used to delineate predictive factors.

297 iance and receiver operating characteristic (ROC) curves were used to determine the significance of e

298 -art gkmSVM-2.0 algorithms in area under the ROC curve, while achieving average speedups in kernel co

299 Area under the ROC curve with stress MBF and MPR as the outcome measure

300 ptimal models for months 1, 2, and 3 yielded ROC curves with AUCs of 0.68 (95% CI: 0.63, 0.74), 0.75