ライフサイエンスコーパス: receiver operating characteristic

1 We used logistic regressions and receiver operating characteristic analyses to evaluate h

2 rimination markers between CAP and AECOPD in receiver operating characteristic analyses, with an area

3 correct top diagnosis (TDx), as well as with receiver operating characteristic analyses.

4 Furthermore, receiver operating characteristics analyses revealed tha

5 Receiver-operating-characteristic analyses showed (18)F-

6 eters to predict outcome were established by receiver-operating-characteristic analyses using a media

7 curacies of PET parameters were evaluated by receiver-operating-characteristic analyses using the cli

8 Receiver operating characteristic analysis confirmed tha

9 Receiver operating characteristic analysis identified op

10 s in patients of all ages with cystinosis; a receiver operating characteristic analysis ranked chitot

11 Receiver operating characteristic analysis revealed comb

12 Receiver operating characteristic analysis showed an are

13 Receiver operating characteristic analysis showed that t

14 Further, receiver operating characteristic analysis shows that ou

15 We performed receiver operating characteristic analysis to define opt

16 Receiver operating characteristic analysis was performed

17 A Mann-Whitney U test and receiver operating characteristic analysis was performed

18 Receiver operating characteristic analysis was used to d

19 Receiver operating characteristic analysis was used to e

20 Receiver operating characteristic analysis with logistic

21 ht independent observers were measured using receiver operating characteristic analysis, linearly wei

22 Independent predictors were assessed through receiver operating characteristic analysis, time-series

23 formance was analyzed by using free-response receiver operating characteristic analysis.

24 in risk starting at 3 days as determined by receiver operating characteristic analysis.

25 Receiver operating characteristics analysis correctly as

26 Receiver-operating characteristic analysis demonstrated

27 dex were analyzed using Pearson coefficient, receiver-operating characteristics analysis and by univa

28 of (18)F-FET PET parameters was evaluated by receiver-operating-characteristic analysis and chi(2) te

29 their ability to predict amyloid status in a receiver-operating-characteristic analysis and validated

30 sis of volumes of interest and examined with receiver-operating-characteristic analysis to determine

31 Receiver-operating-characteristic analysis was performed

32 Receiver-operating-characteristic analysis was used to d

33 For differentiating TP from TRCs, receiver-operating-characteristic analysis yielded an op

34 At receiver-operating-characteristic analysis, an (18)F-FDG

35 model was described using the area under the receiver operating characteristic and average precision

36 Receiver operating characteristic and net reclassificati

37 aving higher area under the curve values for receiver operating characteristic and precision-recall c

38 -month waiting-list survival was assessed by receiver operating characteristics and net reclassificat

39 We assessed discriminatory performance by receiver operating characteristics and tumour extent pre

40 clinical variables yielded a cross-validated receiver operating characteristic area under the curve (

41 Model performance was evaluated using receiver operating characteristic area under the curve (

42 The receiver operating characteristic area under the curve b

43 rentiated AD from both clinically diagnosed (receiver operating characteristic area under the curve o

44 th a highly significant relationship to UOC (Receiver operating characteristic-area under the curve:

45 e (PPV) (63.5%), and area under the curve of receiver operating characteristics (AUC ROC) (0.978).

46 he scores was evaluated using area under the receiver operating characteristic (AUROC) curve and C-st

47 ll carcinoma (RCC) in plasma (area under the receiver operating characteristic (AUROC) curve of 0.99)

48 The area under the receiver operating characteristics (AUROC) curve for sep

49 n, multiple regression models and area under receiver-operating characteristic (AUROC) curves were us

50 d, the ensemble model yielded area under the receiver-operating-characteristic (auROC) scores of 0.73

51 d baseline and change in GGT (area under the receiver operating characteristic [AUROC], 0.79; 95% con

52 ated good discrimination (all area under the receiver operating characteristic curve >= 0.84) and cal

53 xcellent discrimination (both area under the receiver operating characteristic curve >= 0.85), but po

54 orizons up to 8 years of age (area under the receiver operating characteristic curve >= 0.9), doubles

55 emia had significantly higher area under the receiver operating characteristic curve (0.78 [95% CI 0.

56 An receiver operating characteristic curve (area under the

57 odels were assessed using the area under the receiver operating characteristic curve (AROC).

58 nce of CT was estimated using area under the receiver operating characteristic curve (AUC) analysis a

59 Performance was measured by area under receiver operating characteristic curve (AUC) analysis.

60 Area under the receiver operating characteristic curve (AUC) and area u

61 rformance was assessed by the area under the receiver operating characteristic curve (AUC) and differ

62 rmance was evaluated based on area under the receiver operating characteristic curve (AUC) and label

63 nesses were calculated as the area under the receiver operating characteristic curve (AUC) and Pearso

64 the SJLIFE cohort) using the areas under the receiver operating characteristic curve (AUC) and the pr

65 Area under the receiver operating characteristic curve (AUC) difference

66 Area under the receiver operating characteristic curve (AUC) for ADC, D

67 Area under the receiver operating characteristic curve (AUC) for T2-wei

68 sensitivity, specificity and area under the receiver operating characteristic curve (AUC) for the di

69 The area under the receiver operating characteristic curve (AUC) for the im

70 acetylneuraminate achieved an area under the receiver operating characteristic curve (AUC) of 0.66 at

71 initial study visits with an area under the receiver operating characteristic curve (AUC) of 0.71 an

72 on, the algorithm achieved an area under the receiver operating characteristic curve (AUC) of 0.84 (8

73 d approach achieved values of area under the receiver operating characteristic curve (AUC) of 0.89 (9

74 atients from controls with an area under the receiver operating characteristic curve (AUC) of 0.896,

75 nt prediction of HAD with the area under the receiver operating characteristic curve (AUC) of 89.4% i

76 We used the area under the receiver operating characteristic curve (AUC) to evaluat

77 Area under the receiver operating characteristic curve (AUC) values for

78 .1 v > 13.1), as indicated by area under the receiver operating characteristic curve (AUC) values of

79 .64), respectively, and with areas under the receiver operating characteristic curve (AUC) values of

80 Area under receiver operating characteristic curve (AUC) values wer

81 The area under the receiver operating characteristic curve (AUC) was calcul

82 Statistics for the area under the receiver operating characteristic curve (AUC) were 0.70

83 OCT parameters with the best area under the receiver operating characteristic curve (AUC) were deter

84 e models were evaluated using area under the receiver operating characteristic curve (AUC) with cross

85 rosis was quantified by using area under the receiver operating characteristic curve (AUC) with quant

86 mparisons were made using the area under the receiver operating characteristic curve (AUC), a measure

87 ance was summarized using the area under the receiver operating characteristic curve (AUC), calculate

88 Area under the receiver operating characteristic curve (AUC), sensitivi

89 Metrics included area under the receiver operating characteristic curve (AUC), sensitivi

90 s smaller than 50 mm by using area under the receiver operating characteristic curve (AUC).

91 y and the DeLong test for the area under the receiver operating characteristic curve (AUC).

92 essed independently using the area under the receiver operating characteristic curve (AUC).

93 all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for th

94 Areas under the receiver operating characteristic curve (AUCs) for a com

95 ients; 10 782 foci), the CNN areas under the receiver operating characteristic curve (AUCs) for deter

96 PDAC stage I-II samples, the areas under the receiver operating characteristic curve (AUCs) increased

97 , and 3) performed well with areas under the receiver operating characteristic curve (AUCs) of 0.91,

98 Areas under the receiver operating characteristic curve (AUCs) were 0.84

99 The odds ratios and areas under the receiver operating characteristic curve (AUCs) were high

100 ctor and their corresponding areas under the receiver operating characteristic curve (AUCs) were obta

101 f lesion detection rates and areas under the receiver operating characteristic curve (AUCs).

102 this high-risk patient group: area under the receiver operating characteristic curve (AUROC) 0.93, ca

103 ted failed revascularization: area under the receiver operating characteristic curve (AUROC) 0.95, ca

104 ted NASH with cross-validated area under the receiver operating characteristic curve (AUROC) = 0.73,

105 s collected from DrugCentral [area under the receiver operating characteristic curve (AUROC) = 0.868]

106 y score <3) was assessed by using area under receiver operating characteristic curve (AUROC) analysis

107 Models were evaluated on area under the receiver operating characteristic curve (AUROC) and area

108 fied patients with BE with an area under the receiver operating characteristic curve (AuROC) of 0.579

109 and CN-high-like ECs with an area under the receiver operating characteristic curve (AUROC) of 0.78

110 4% accuracy and 0.95 +/- 0.02 area under the receiver operating characteristic curve (AUROC).

111 covered mutual targets for drugs [area under Receiver Operating Characteristic curve (AUROC)=0.75] an

112 Areas under the receiver operating characteristic curve (AUROCs) were us

113 with 10-fold cross-validated areas under the receiver operating characteristic curve (cvAUCs), and th

114 IRT achieves an area under the receiver operating characteristic curve (ROC-AUC) of 0.7

115 ls, particularly ANN with the area under the receiver operating characteristic curve (ROC-AUC) of 0.7

116 0.94 [0.88-0.99]) and SBFBT (area under the receiver operating characteristic curve 0.83 [0.73-0.93]

117 emia had high discrimination (area under the receiver operating characteristic curve 0.88 [95% CI 0.8

118 DeltaSBF/DeltaT (area under the receiver operating characteristic curve 0.94 [0.88-0.99]

119 tablets' front-facing camera (area under the receiver operating characteristic curve = 0.78).

120 dent predictors of phenotype (area under the receiver operating characteristic curve = 0.95).

121 ion according to maximum SUV (area under the receiver operating characteristic curve = 100%; 95% conf

122 : 99%, 100%) and minimum ADC (area under the receiver operating characteristic curve = 98%; 95% CI: 9

123 pproach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and

124 ed reasonable discrimination (area under the receiver operating characteristic curve [AUC] = 0.71) an

125 cy to define disease by mPAP (area under the receiver operating characteristic curve [AUC], 0.78) and

126 th the discovery cohort (mean area under the receiver operating characteristic curve [AUC], 0.89; 95%

127 pes in the validation cohort (area under the receiver operating characteristic curve [AUC], 0.95; 95%

128 high diagnostic performance (area under the receiver operating characteristic curve [AUC], 0.97; 95%

129 tion of the objective tools (SORT Area Under Receiver Operating Characteristic curve [AUROC] = 0.90,

130 inadequate for clinical use (area under the receiver operating characteristic curve [AUROC], < 0.8),

131 Receiver operating characteristic curve analyses for the

132 ic and non-amnestic Alzheimer's disease, and receiver operating characteristic curve analyses indicat

133 Hazard ratios (HRs) and receiver operating characteristic curve analyses were pe

134 promising antigens were identified based on receiver operating characteristic curve analysis (CBU_17

135 Receiver operating characteristic curve analysis and con

136 Receiver operating characteristic curve analysis for cal

137 Receiver operating characteristic curve analysis of the

138 Receiver operating characteristic curve analysis of thes

139 A receiver operating characteristic curve analysis showed

140 Performance was evaluated by area under the receiver operating characteristic curve analysis, sensit

141 ual to 100 Gy (best cut-off according to the receiver operating characteristic curve and median tumor

142 nce without and with PRSs via area under the receiver operating characteristic curve and net reclassi

143 predictive performances with areas under the receiver operating characteristic curve and precision re

144 classification had a greater area under the receiver operating characteristic curve and reclassified

145 Specifically, receiver operating characteristic curve areas (AUC) for

146 Receiver operating characteristic curve demonstrated tha

147 Area under the receiver operating characteristic curve for Ct vs positi

148 sIn DS1, population-adjusted areas under the receiver operating characteristic curve for pneumothorax

149 The area under the receiver operating characteristic curve for renal resist

150 The median (SD) of the area under the receiver operating characteristic curve for the natural

151 The MSKCC model receiver operating characteristic curve had a predictive

152 Receiver operating characteristic curve identified >15.5

153 characteristics by achieving area under the receiver operating characteristic curve improvements of

154 ements and Main Results: The areas under the receiver operating characteristic curve in the external

155 ing-based genetic algorithm, with an overall receiver operating characteristic curve in the internal

156 rformance was assessed by the area under the receiver operating characteristic curve in the validatio

157 and procedures yielded a mean area under the receiver operating characteristic curve of 0.76 (ranging

158 enous thromboembolism with an area under the receiver operating characteristic curve of 0.760 (95% CI

159 %, specificity 71.1%, with an area under the receiver operating characteristic curve of 0.80.

160 as COVID-19 pneumonia with an area under the receiver operating characteristic curve of 0.81.

161 (<=1 year or >1 year) with an area under the receiver operating characteristic curve of 0.86 (sensiti

162 2 years corrected age with an area under the receiver operating characteristic curve of 0.86, 0.66 an

163 f the clinical trial, with an area under the receiver operating characteristic curve of 0.86.Conclusi

164 a median discriminating power area under the receiver operating characteristic curve of 0.883 (95% CI

165 rithm identified LVSD with an area under the receiver operating characteristic curve of 0.89 (95% CI,

166 ier yielded a cross-validated area under the receiver operating characteristic curve of 0.89 (95% con

167 88) on the test data, with an area under the receiver operating characteristic curve of 0.91 (95% CI:

168 %, specificity 88.7%, with an area under the receiver operating characteristic curve of 0.91.

169 l validation datasets with an area under the receiver operating characteristic curve of 0.912 (95% CI

170 with and without irSAEs with area under the receiver operating characteristic curve of 0.92 (95% con

171 8%, specificity 93.9%, and an area under the receiver operating characteristic curve of 0.93 in the t

172 , classified tumor type with areas under the receiver operating characteristic curve of 0.94 (95% con

173 h in advance, resulting in an area under the receiver operating characteristic curve of 0.94 and an a

174 those without COVID-19, with areas under the receiver operating characteristic curve of 0.95 (95% CI:

175 The area under the curve of the receiver operating characteristic curve of the highest e

176 The area under the receiver operating characteristic curve of the LUCK clas

177 The area under the receiver operating characteristic curve of the model was

178 The area under the receiver operating characteristic curve on the Family Co

179 rongest predictive potential (area under the receiver operating characteristic curve values 0.62-0.73

180 The area under the receiver operating characteristic curve was 0.88 (0.86-0

181 of 51; 95% CI: 65%, 89%), and area under the receiver operating characteristic curve was 0.88 (95% CI

182 The area under the receiver operating characteristic curve was assessed to

183 With ChestX-ray14, areas under the receiver operating characteristic curve were 0.94 (95% C

184 sensitivity, specificity, and area under the receiver operating characteristic curve were 83% (95% co

185 is, Kaplan-Meier curves, and cross-validated receiver operating characteristic curve with area under

186 ision) and the DeLong method (area under the receiver operating characteristic curve).

187 g lipid-poor angiomyolipomas (area under the receiver operating characteristic curve, >0.9), indicati

188 ity = 41%, specificity = 88%, area under the receiver operating characteristic curve, 0.64).

189 the hysteresis ratio was 28% (area under the receiver operating characteristic curve, 0.80; 95% CI, 0

190 ity = 77%, specificity = 97%, area under the receiver operating characteristic curve, 0.87) than the

191 n ejection fraction <50%, the area under the receiver operating characteristic curve, accuracy, sensi

192 erformance is presented as an area under the receiver operating characteristic curve.

193 measured by the area under the curve of the receiver operating characteristic curve.

194 y: 92.9%; sensitivity: 67.1%; area under the receiver operating characteristic curve: 0.83; p < 0.000

195 e days of hospital admission (area under the receiver operating characteristic curve=0.80 (95%CI 0.75

196 The areas under the receiver operating characteristics curve (95% confidence

197 on the entire dataset provided an area under receiver operating characteristics curve (AUC) with 95%

198 by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was high

199 ween 1.0 and 1.4 mm diameters had area under receiver operating characteristics curve (AUROC) values

200 Moreover, in the receiver operating characteristics curve analyses tear o

201 The area under the receiver operating characteristics curve for P0.1vent to

202 RC as three input variables, the area under receiver operating characteristics curve for predicting

203 The area under the receiver operating characteristics curve for the model w

204 The AI achieved an area under the receiver operating characteristics curve of 0.997 (95% C

205 Analysis of area under the receiver operating characteristics curve revealed that P

206 The area under receiver operating characteristics curve was 0.77 (95% c

207 ier with a bacterial-vs-other area under the receiver-operating characteristic curve (AUROC) 0.92 (95

208 el had better discrimination (area under the receiver-operating characteristic curve [AUC]) for incid

209 Receiver-operating characteristic curve analysis demonst

210 The per patient area under the receiver-operating characteristic curve analysis was 0.8

211 al intelligence algorithm were quantified by receiver-operating characteristic curve analysis.

212 equirement by calculating the area under the receiver-operating characteristic curve and by classific

213 The area under the receiver-operating characteristic curve for CT-FFR was 0

214 8.6%), with a partial area under the summary receiver-operating characteristic curve of 0.420 (I(2) =

215 8.3%), with a partial area under the summary receiver-operating characteristic curve of 0.686 (I(2) =

216 2-0.817) versus radiologist's area under the receiver-operating characteristic curve of 0.698 (0.646-

217 ithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.737 (0.659-

218 ithm: artificial intelligence-area under the receiver-operating characteristic curve of 0.740 (0.662-

219 9-0.815) versus radiologist's area under the receiver-operating characteristic curve of 0.779 (0.723-

220 or 30-day oxygen requirement (area under the receiver-operating characteristic curve, 0.84; 95% CI, 0

221 30-day intubation/mortality (area under the receiver-operating characteristic curve, 0.86; 95% CI, 0

222 infected persons, assessed by area under the receiver-operating characteristic curve, exceeded 85%.

223 ansferase showing the highest area under the receiver-operating characteristics curve (0.84).

224 Receiver-operating characteristics curve analyses were u

225 sed to estimate the area under the localized receiver-operating-characteristic curve (ALROC).

226 rmediate-to-high ADNC with an area under the receiver-operating-characteristic curve (AUC) of 0.80, 0

227 Time-dependent receiver-operating-characteristic curve analysis provide

228 Receiver-operating-characteristic curve analysis was use

229 Sensitivity, specificity, and area under the receiver-operating-characteristic curve for peribronchov

230 were highly accurate, with an area under the receiver-operating-characteristic curve of more than 0.9

231 sensitivity, specificity, and area under the receiver-operating-characteristic curve were significant

232 T quantitative methods had an area under the receiver-operating-characteristics curve ranging from mo

233 The area under the receiver operating characteristic curves (AUC) and parti

234 s was determined by comparing area under the receiver operating characteristic curves (AUC).

235 s assessed by calculating the area under the receiver operating characteristic curves (AUC).

236 score produced the following areas under the receiver operating characteristic curves (AUCs): 0.80 (9

237 tion cohort of 402 patients, areas under the receiver operating characteristic curves (AUROC) of HHPA

238 mepoints were evaluated using area under the receiver operating characteristic curves (AUROCs).

239 pic asthma with reporting of areas under the receiver operating characteristic curves as a measure of

240 The area under the receiver operating characteristic curves for coprevalent

241 Comparisons were made by using receiver operating characteristic curves for diagnostic

242 age groups for serological monitoring using receiver operating characteristic curves for different e

243 was poor with respectively an area under the receiver operating characteristic curves of 0.57 (95% CI

244 nonatopic participants with areas under the receiver operating characteristic curves of at least 0.8

245 and external validation, the area under the receiver operating characteristic curves of the DLA with

246 nt Method for ICU resulted in area under the receiver operating characteristic curves that were not s

247 Area under the receiver operating characteristic curves were calculated

248 These area under the receiver operating characteristic curves were lower than

249 Receiver operating characteristic curves were plotted to

250 rs and severity of glaucoma was examined and Receiver Operating Characteristic curves were used to as

251 Receiver operating characteristic curves were used to de

252 Receiver Operating Characteristic curves were used to de

253 Logistic regression and receiver operating characteristic curves were used to ev

254 A multivariate analysis and the receiver operating characteristic curves were used to in

255 Performance was measured using receiver operating characteristic curves, adjusting for

256 Using receiver operating characteristic curves, we obtained op

257 ography as gold standard, were defined using receiver operating characteristic curves.

258 ffs to maximize diagnostic performance using receiver operating characteristic curves; and 3) bootstr

259 The respective areas under the receiver operating characteristics curves for the cluste

260 l, and markedly increased the area under the receiver-operating characteristic curves of obstructive

261 Areas under receiver-operating-characteristic curves were calculated

262 of the patients with sCD14 levels above the receiver operating characteristics cutoff were deceased

263 by using jackknife alternative free-response receiver operating characteristic figure of merit (FOM)

264 isting of 17 variables had an area under the receiver operating characteristic of 0.80 (95% CI, 0.78-

265 ing of eight variables had an area under the receiver operating characteristic of 0.96 (95% CI, 0.91-

266 f 0.779 (0.723-0.836), diagnostic metrics of receiver-operating characteristic operating points did n

267 accuracy) were calculated based on different receiver-operating characteristic operating points.

268 6-0.749) with similar diagnostic metrics for receiver-operating characteristic operating points.

269 f KLK8 in CSF and blood was determined using receiver operating characteristic (ROC) analyses and com

270 Receiver operating characteristic (ROC) analysis along w

271 Receiver operating characteristic (ROC) analysis was per

272 Receiver operating characteristic (ROC) analysis, odds r

273 er diagnostic performance was evaluated with receiver operating characteristic (ROC) analysis, with t

274 nd the threshold ADC values were computed by receiver operating characteristic (ROC) analysis.

275 s carried out using the hierarchical summary receiver operating characteristic (ROC) and the bivariat

276 ing lactose challenge with an area under the receiver operating characteristic (ROC) curve (AUC) of 1

277 Receiver operating characteristic (ROC) curve analysis d

278 parameter, we calculated the area under the receiver operating characteristic (ROC) curve and the se

279 The area under the receiver operating characteristic (ROC) curve was 0.97 i

280 A receiver operating characteristic (ROC) curve was carrie

281 The area under the receiver operating characteristic (ROC) curves (AUCs) on

282 Receiver operating characteristic (ROC) curves analysis

283 redictive performance was assessed using the receiver operating characteristic (ROC) curves and area

284 erformance over existing methods in terms of receiver operating characteristic (ROC) curves in high-d

285 We assessed discrimination using receiver operating characteristic (ROC) curves, calibrat

286 cant slopes of SD OCT change was assessed by receiver operating characteristic (ROC) curves.

287 Diagnostic accuracy was evaluated using Receiver Operating Characteristic (ROC) curves.

288 es and optimal thresholds were calculated by receiver operating characteristic (ROC) curves.

289 GRS in SLE risk prediction was evaluated by receiver operating characteristic (ROC) curves.

290 Models were evaluated using receiver operating characteristic (ROC) curves.

291 nd Mehralivand EPE score were compared using receiver operating characteristics (ROC) and decision cu

292 Moreover, the receiver operating characteristics (ROC) curve analyses

293 In the validation dataset, the receiver operating characteristics (ROC) were compared b

294 Receiver-operating characteristic (ROC) curves were then

295 rison with young-control SUV ratios (SUVRs), receiver-operating-characteristic (ROC) curves based on

296 ntly predictive of the therapeutic response (receiver operating characteristic [ROC] curve, area unde

297 Receiver operating characteristics set pressure threshol

298 Lesion detectability was measured using a receiver-operating-characteristic study and quantified u

299 the survival scatter plot, the hazard ratio receiver operating characteristic, the area between curv

300 The Receiver Operating Characteristic was adjusted for ICU a