“…A neural network was used to discriminate between signal and background events in the signal-rich regions. …”
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“…The predictability of discriminating urosepsis stages was assessed by using the area under the ROC curve (AUC) and very good specificity and sensitivity was identified in predicting the risk of death for PCT (69.57%, 77.33%), the SOFA (91.33%, 76.82%), qSOFA (91.30%, 74.17%) scores, and CCI (65.22%, 88.74%). …”
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“…Le TAB sur la base du CD203 discrimine mieux que sur la base CD63, faisant s’interroger sur un mécanisme autre que IgE-médié : l’ARN se fixe sur les récepteurs des lymphocytes T (TLR) avec possible activation directe ou participation de la voie MGPRX2.…”
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“…Symptomatic features of this group were characterized by statistically significant differences from the OAB, IC/BPS and control groups on questionnaires, comprehensive review of discriminate pelvic exam, and thematic analysis of patient histories. …”
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“…The performance of V6-RWPT and V6-V1 interpeak interval in discriminating between LBBP and LVSP was assessed using the receiver operating characteristic (ROC) curve. …”
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“…Safe prediction models can help optimize ROP screening by effectively discriminating high-risk from low-risk infants. OBJECTIVE: To evaluate the prognostic value of PND on ROP; to update and validate the Digital ROP (DIGIROP) 2.0 birth into prescreen and screen prediction models to include all ROP-screened infants regardless of gestational age (GA) and incorporate PND; and to compare the DIGIROP model with the Weight, IGF-1, Neonatal, and ROP (WINROP) and Postnatal Growth and ROP (G-ROP) models. …”
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“…These findings suggest that Western guidelines seem applicable in Asian patients and, most importantly, that indexed LV parameters with a lower cutoff could be used in discriminating patients with excess mortality risk.…”
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“…Based on selected radiomic features, six machine learning algorithms including support vector machine with the linear kernel (SVM_L), support vector machine with radial basis function kernel (SVM_RBF), logistic regression (LR), Naïve Bayes (NB), K-nearest neighbors (KNN), and linear discriminant analysis (LDA) were compared to predict the possibility of BRAF(V600E). …”
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“…CONCLUSIONS: In the clinical setting, CSF SNAP-25 is a viable alternative to t-tau, 14–3-3, and the t-tau/p-tau ratio in discriminating the CJD subtypes from other RPDs. Additionally, SNAP-25 and, to a lesser extent, Ng predict survival in CJD, showing prognostic power in the range of CSF t-tau/14–3-3 and NfL, respectively. …”
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“…We found that low vinculin (VCL) and cortactin (CTTN) mRNA expression predicts favorable survival rates and has diagnostic value to discriminate between Tz-sensible and Tz-resistant HER2+ BC patients. …”
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“…DISCUSSION: NfL increase discriminates individuals who convert to prodromal FTD from those who do not, preceding significant frontotemporal MRI volume loss. …”
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“…CRITICAL RELEVANCE STATEMENT: Radiomic features help to identify the most discriminating imaging signs using random forest. ‘Median’ attenuation value (Hounsfield units), extracted from 3D-segmentations on contrast-enhanced chest-CTs, could distinguish carcinoids from atypical hamartomas (AUC = 0.85), was reproducible (ICC = 0.97), and generalized to an external dataset. …”
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“…In an epoch-by-epoch concordance analysis, the bedside radar performed better in discriminating sleep versus wake (Matthew correlation coefficient [MCC]: mean 0.63, SD 0.12, 95% CI 0.57-0.69) than the undermattress devices (MCC of WSA: mean 0.41, SD 0.15, 95% CI 0.36-0.46; MCC of Emfit: mean 0.35, SD 0.16, 95% CI 0.26-0.43). …”
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“…Receiver operating characteristic (ROC) and area under the curve (AUC) analyses confirmed miR-155 accuracy in discriminating P, AS-C and AS-P groups (AUC 0.6861–0.9944, p < 0.0001–0.05), coupled with high sensitivity (76.7–100.0%), specificity (53.3–96.7%) and cut-off points (> 0.955- > 2.915 a.u.; p < 0.0001). miR-155 levels further distinguished between CHD (AS-C, AS-P) and periodontitis (P) patients (AUC ≥ 0.8378, sensitivity ≥ 88.7%, specificity ≥ 73.3%, cut-off > 2.82 a.u; p < 0.0001), and between AS-C and AS-P patients (AUC 0.7578, sensitivity 80.0%, specificity 50.0%, cut-off > 7.065 a.u; p < 0.001). …”
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“…Thus, we believe that the discriminating between MBL and serine-CPE is important. …”
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“…Confirmatory composite analysis showed high reliability and discriminant and convergent validity for most Brief MAIA-2 scales, except Noticing. …”
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“…The first one, performed via ROC (Receiver Operating Curve) analysis, aims at assessing the intrinsic ability of the methodology to discriminate and classify biological sequences and structures. …”
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“…The clustering and ordination analyses implemented in Discriminant Analysis of Principal Components (DAPC) and STRUCTURE showed mostly concordant groupings and a high degree of differentiation among groups. …”
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“…Receiver operator curves (ROCs) were used to explore the discriminative accuracy of preoperative PROs (Total WOMAC Knee Score, IKDC Subjective Knee Form, and Lysholm Knee Scale) for identifying patients reporting to be able to do “nearly everything” or “everything” at the last available follow-up. …”
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“…Secondly, we have created patient groups based on the patients' activity ranks (ASDAS-CRP and SASDAS categorisation) within the cohort to assess discriminative accuracy. Additionally, to distinguish patients with active and non-active disease and to assess their respective cut-off points values, the receiver operating characteristic (ROC) curve analysis was used. …”
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