scholarly journals Ion mobility spectrometry nuisance alarm threshold analysis for illicit narcotics based on environmental background and a ROC-curve approach

The Analyst ◽  
2016 ◽  
Vol 141 (14) ◽  
pp. 4438-4446 ◽  
Author(s):  
Thomas P. Forbes ◽  
Marcela Najarro
Keyword(s):  
Receiver Operating Characteristic ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Threshold Analysis ◽  
Trace Detection ◽  
Ion Mobility Spectrometer ◽  
Environmental Background ◽  
Alarm Threshold

The discriminative potential of an ion mobility spectrometer (IMS) for trace detection of illicit narcotics relative to environmental background was investigated with a receiver operating characteristic (ROC) curve framework.

scholarly journals Discriminative potential of ion mobility spectrometry for the detection of fentanyl and fentanyl analogues relative to confounding environmental interferents

The Analyst ◽  
2019 ◽  
Vol 144 (21) ◽  
pp. 6391-6403 ◽  
Author(s):  
Thomas P. Forbes ◽  
Jeffrey Lawrence ◽  
Jennifer R. Verkouteren ◽  
R. Michael Verkouteren
Keyword(s):  
Receiver Operating Characteristic ◽  
Ion Mobility Spectrometry ◽  
Ion Mobility ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Trace Detection ◽  
Environmental Background ◽  
Fentanyl Analogues ◽  
Related Compounds ◽  
Receiver Operating

Receiver operating characteristic (ROC) curve framework was employed to investigate the trace detection of fentanyl and fifteen fentanyl-related compounds relative to environmental background interferents.

scholarly journals Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes

2012 ◽  
Vol 1 (3) ◽  
pp. 145-150
Author(s):  
Bernardo Lopes ◽  
Allan Luz ◽  
Bruno Fontes ◽  
Isaac C Ramos ◽  
Fernando Correia ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Receiver Operating Characteristic ◽  
Linear Model ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Predictive Accuracy ◽  
Corneal Resistance Factor ◽  
Corneal Deformation ◽  
Eye Examination ◽  
The Individual

ABSTRACT Purpose To compare and assess the ability of pressure-derived parameters and corneal deformation waveform signal-derived parameters of the ocular response analyzer (ORA) measurement to distinguish between keratoconus and normal eyes, and to develop a combined parameter to optimize the diagnosis of keratoconus. Materials and methods One hundred and seventy-seven eyes (177 patients) with keratoconus (group KC) and 205 normal eyes (205 patients; group N) were included. One eye from each subject was randomly selected for analysis. Patients underwent a complete clinical eye examination, corneal topography (Humphrey ATLAS), tomography (Pentacam Oculus) and biomechanical evaluations (ORA Reichert). Differences in the distributions between the groups were assessed using the Mann- Whitney test. The receiver operating characteristic (ROC) curve was used to identify cutoff points that maximized sensitivity and specificity in discriminating keratoconus from normal corneas. Logistic regression was used to identify a combined linear model (Fisher 1.0). Results Significant differences in all studied parameters were detected (p < 0.05), except for W2. For the corneal resistance factor (CRF): Area under the ROC curve (AUROC) 89.1%, sensitivity 81.36%, specificity 84.88%. For the p1area: AUROC 91.5%, sensitivity 87.1%, specificity 81.95%. Of the individual parameters, the highest predictive accuracy was for the Fisher 1.0, which represents the combination of all parameters (AUROC 95.5%, sensitivity 88.14%, specificity 93.17%). Conclusion Waveform-derived ORA parameters displayed greater accuracy than pressure-derived parameters for identifying keratoconus. Corneal hysteresis (CH) and CRF, a diagnostic linear model that combines different parameters, provided the greatest accuracy for differentiating keratoconus from normal corneas. How to cite this article Luz A, Fontes B, Ramos IC, Lopes B, Correia F, Schor P, Ambrósio R. Evaluation of Ocular Biomechanical Indices to Distinguish Normal from Keratoconus Eyes. Int J Kerat Ect Cor Dis 2012;1(3):145-150.

scholarly journals Comparative Assessment of Three Common Algorithms for Estimating the Variance of the Area under the Nonparametric Receiver Operating Characteristic Curve

2002 ◽  
Vol 2 (3) ◽  
pp. 280-289 ◽  
Author(s):  
Mario A. Cleves
Keyword(s):  
Receiver Operating Characteristic ◽  
Diagnostic Test ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Simulated Data ◽  
Small Samples ◽  
Binary Outcomes ◽  
Discriminatory Accuracy ◽  
Receiver Operating

The area under the receiver operating characteristic (ROC) curve is often used to summarize and compare the discriminatory accuracy of a diagnostic test or modality, and to evaluate the predictive power of statistical models for binary outcomes. Parametric maximum likelihood methods for fitting of the ROC curve provide direct estimates of the area under the ROC curve and its variance. Nonparametric methods, on the other hand, provide estimates of the area under the ROC curve, but do not directly estimate its variance. Three algorithms for computing the variance for the area under the nonparametric ROC curve are commonly used, although ambiguity exists about their behavior under diverse study conditions. Using simulated data, we found similar asymptotic performance between these algorithms when the diagnostic test produces results on a continuous scale, but found notable differences in small samples, and when the diagnostic test yields results on a discrete diagnostic scale.

scholarly journals Platelet and Red Blood Cell Indices in Patients with Panic Disorder: A Receiver Operating Characteristic Analysis

2020 ◽  
Vol 11 (02) ◽  
pp. 261-266 ◽  
Author(s):  
Ramdas S. Ransing ◽  
Neha Gupta ◽  
Girish Agrawal ◽  
Nilima Mahapatro
Keyword(s):  
Panic Disorder ◽  
Blood Cell ◽  
Receiver Operating Characteristic ◽  
Red Blood Cell ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Healthy Controls ◽  
Distribution Width ◽  
Rbc Indices ◽  
Receiver Operating

Abstract Objective Panic disorder (PD) is associated with changes in platelet and red blood cell (RBC) indices. However, the diagnostic or predictive value of these indices is unknown. This study assessed the diagnostic and discriminating value of platelet and RBC indices in patients with PD. Materials and Methods In this cross-sectional study including patients with PD (n = 98) and healthy controls (n = 102), we compared the following blood indices: mean platelet volume (MPV), platelet distribution width (PDW), and RBC distribution width (RDW). The receiver operating characteristic (ROC) curve was used to calculate the area under the ROC curve (AUC), sensitivity, specificity, and likelihood ratio for the platelet and RBC indices. Results Statistically significant increase in PDW (17.01 ± 0.91 vs. 14.8 ± 2.06; p < 0.0001) and RDW (16.56 ± 2.32 vs. 15.12 ± 2.43; p < 0.0001) levels were observed in patients with PD. PDW and mean corpuscular hemoglobin concentration had larger AUC (0.89 and 0.74, respectively) and Youden’s index (0.65 and 0.39, respectively), indicating their higher predictive capacity as well as higher sensitivity in discriminating patients with PD from healthy controls. Conclusion PDW can be considered a “good” diagnostic or predictive marker in patients with PD.

Intraoperative scrape cytology: Comparison with frozen sections, using receiver operating characteristic (ROC) curve

2000 ◽  
Vol 23 (2) ◽  
pp. 134-139 ◽  
Author(s):  
Vinod Shidham ◽  
Dilip Gupta ◽  
Lorenzo M. Galindo ◽  
Marian Haber ◽  
Carolyn Grotkowski ◽  
...  
Keyword(s):  
Receiver Operating Characteristic ◽  
Roc Curve ◽  
Operating Characteristic ◽  
Frozen Sections ◽  
Scrape Cytology ◽  
Receiver Operating
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