Relevant bibliographies by topics / AREA UNDER THE ROC-CURVE (AUC)

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'AREA UNDER THE ROC-CURVE (AUC)'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "AREA UNDER THE ROC-CURVE (AUC)"

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Janssens, A. Cecile J. W., and Forike K. Martens. "Reflection on modern methods: Revisiting the area under the ROC Curve." International Journal of Epidemiology 49, no. 4 (2020): 1397–403. http://dx.doi.org/10.1093/ije/dyz274.

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Abstract The area under the receiver operating characteristic (ROC) curve (AUC) is commonly used for assessing the discriminative ability of prediction models even though the measure is criticized for being clinically irrelevant and lacking an intuitive interpretation. Every tutorial explains how the coordinates of the ROC curve are obtained from the risk distributions of diseased and non-diseased individuals, but it has not become common sense that therewith the ROC plot is just another way of presenting these risk distributions. We show how the ROC curve is an alternative way to present risk
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Marzban, Caren. "The ROC Curve and the Area under It as Performance Measures." Weather and Forecasting 19, no. 6 (2004): 1106–14. http://dx.doi.org/10.1175/825.1.

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Abstract The receiver operating characteristic (ROC) curve is a two-dimensional measure of classification performance. The area under the ROC curve (AUC) is a scalar measure gauging one facet of performance. In this short article, five idealized models are utilized to relate the shape of the ROC curve, and the area under it, to features of the underlying distribution of forecasts. This allows for an interpretation of the former in terms of the latter. The analysis is pedagogical in that many of the findings are already known in more general (and more realistic) settings; however, the simplicit
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Narasimhan, Harikrishna, and Shivani Agarwal. "Support Vector Algorithms for Optimizing the Partial Area under the ROC Curve." Neural Computation 29, no. 7 (2017): 1919–63. http://dx.doi.org/10.1162/neco_a_00972.

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The area under the ROC curve (AUC) is a widely used performance measure in machine learning. Increasingly, however, in several applications, ranging from ranking to biometric screening to medicine, performance is measured not in terms of the full area under the ROC curve but in terms of the partial area under the ROC curve between two false-positive rates. In this letter, we develop support vector algorithms for directly optimizing the partial AUC between any two false-positive rates. Our methods are based on minimizing a suitable proxy or surrogate objective for the partial AUC error. In the
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Cortese, Giuliana. "Estimating the Area under the ROC Curve with Modified Profile Likelihoods." International Journal of Statistics and Probability 6, no. 1 (2016): 1. http://dx.doi.org/10.5539/ijsp.v6n1p1.

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Receiver operating characteristic (ROC) curves are a frequent tool to study the discriminating ability of a certain characteristic. The area under the ROC curve (AUC) is a widely used measure of statistical accuracy of continuous markers for diagnostic tests, and has the advantage of providing a single summary index of overall performance of the test. Recent studies have shown some critical issues related to traditional point and interval estimates for the AUC, especially for small samples, more complex models, unbalanced samples or values near the boundary of the parameter space, i.e., when t
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Ekström, Joakim, Jim Åkerrén Ögren, and Tobias Sjöblom. "Exact Probability Distribution for the ROC Area under Curve." Cancers 15, no. 6 (2023): 1788. http://dx.doi.org/10.3390/cancers15061788.

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The Receiver Operating Characteristic (ROC) is a de facto standard for determining the accuracy of in vitro diagnostic (IVD) medical devices, and thus the exactness in its probability distribution is crucial toward accurate statistical inference. We show the exact probability distribution of the ROC AUC-value, hence exact critical values and p-values are readily obtained. Because the exact calculations are computationally intense, we demonstrate a method of geometric interpolation, which is exact in a special case but generally an approximation, vastly increasing computational speeds. The meth
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Liu, Siyan, Qinglong Tian, Yukun Liu, and Pengfei Li. "Joint Statistical Inference for the Area under the ROC Curve and Youden Index under a Density Ratio Model." Mathematics 12, no. 13 (2024): 2118. http://dx.doi.org/10.3390/math12132118.

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The receiver operating characteristic (ROC) curve is a valuable statistical tool in medical research. It assesses a biomarker’s ability to distinguish between diseased and healthy individuals. The area under the ROC curve (AUC) and the Youden index (J) are common summary indices used to evaluate a biomarker’s diagnostic accuracy. Simultaneously examining AUC and J offers a more comprehensive understanding of the ROC curve’s characteristics. In this paper, we utilize a semiparametric density ratio model to link the distributions of a biomarker for healthy and diseased individuals. Under this mo
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Kochański, Błażej. "The shape of an ROC curve in the evaluation of credit scoring models." Statistics in Transition new series 25, no. 2 (2024): 205–18. http://dx.doi.org/10.59170/stattrans-2024-022.

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The AUC, i.e. the area under the receiver operating characteristic (ROC) curve, or its scaled version, the Gini coefficient, are the standard measures of the discriminatory power of credit scoring. Using binormal ROC curve models, we show how the shape of the curves affects the economic benefits of using scoring models with the same AUC. Based on the results, we propose that the shape parameter of the fitted ROC curve is reported alongside its AUC/Gini whenever the quality of a scorecard is discussed.
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Tang, Ke, Rui Wang, and Tianshi Chen. "Towards Maximizing the Area Under the ROC Curve for Multi-Class Classification Problems." Proceedings of the AAAI Conference on Artificial Intelligence 25, no. 1 (2011): 483–88. http://dx.doi.org/10.1609/aaai.v25i1.7901.

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The Area Under the ROC Curve (AUC) metric has achieved a big success in binary classification problems since they measure the performance of classifiers without making any specific assumptions about the class distribution and misclassification costs. This is desirable because the class distribution and misclassification costs may be unknown during training process or even change in environment. MAUC, the extension of AUC to multi-class problems, has also attracted a lot of attention. However, despite the emergence of approaches for training classifiers with large AUC, little has been done for
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Vardhan, R. Vishnu, and S. Balaswamy. "Improved Methods for Estimating Areas under the Receiver Operating Characteristic Curves." International Journal of Green Computing 4, no. 2 (2013): 58–75. http://dx.doi.org/10.4018/jgc.2013070105.

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ROC Curve is the most widely used statistical technique for classifying an individual into one of the two pre-determined groups basing on test result. Area under the curve (AUC) is a measure of accuracy which exhibits the discriminating power of the test with respect to a threshold or cutoff value. In medical diagnosis, this technique has its relevance to study and compare different diagnostic tests. In this paper, a method is proposed to estimate the AUC of Binormal ROC model by taking into account the confidence interval of mean and corresponding variances.
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Takenouchi, Takashi, Osamu Komori, and Shinto Eguchi. "An Extension of the Receiver Operating Characteristic Curve and AUC-Optimal Classification." Neural Computation 24, no. 10 (2012): 2789–824. http://dx.doi.org/10.1162/neco_a_00336.

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While most proposed methods for solving classification problems focus on minimization of the classification error rate, we are interested in the receiver operating characteristic (ROC) curve, which provides more information about classification performance than the error rate does. The area under the ROC curve (AUC) is a natural measure for overall assessment of a classifier based on the ROC curve. We discuss a class of concave functions for AUC maximization in which a boosting-type algorithm including RankBoost is considered, and the Bayesian risk consistency and the lower bound of the optimu
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Dissertations / Theses on the topic "AREA UNDER THE ROC-CURVE (AUC)"

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Zheng, Shimin. "The ROC Curve and the Area under the Curve (AUC)." Digital Commons @ East Tennessee State University, 2017. https://dc.etsu.edu/etsu-works/139.

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Sun, Fangfang. "Semi-parametric inference for the partial area under the ROC curve." unrestricted, 2008. http://etd.gsu.edu/theses/available/etd-11192008-113213/.

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Thesis (M.S.)--Georgia State University, 2008.<br>Title from file title page. Gengsheng Qin, committee chair; Yu-Sheng Hsu, Yixin Fang, Yuanhui Xiao, committee members. Description based on contents viewed July 22, 2009. Includes bibliographical references (p. 29-30).
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Zhu, Yao-Wei. "LIMITED SAMPLING STRATEGIES FOR FACILE DETERMINATION OF THE AREA UNDER THE CURVE OF ANTI-CANCER AGENTS, PACLITAXEL AND SU5416." University of Cincinnati / OhioLINK, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin984580545.

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Xu, Ping. "Evaluation of Repeated Biomarkers: Non-parametric Comparison of Areas under the Receiver Operating Curve Between Correlated Groups Using an Optimal Weighting Scheme." Scholar Commons, 2012. http://scholarcommons.usf.edu/etd/4261.

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Receiver Operating Characteristic (ROC) curves are often used to evaluate the prognostic performance of a continuous biomarker. In a previous research, a non-parametric ROC approach was introduced to compare two biomarkers with repeated measurements. An asymptotically normal statistic, which contains the subject-specific weights, was developed to estimate the areas under the ROC curve of biomarkers. Although two weighting schemes were suggested to be optimal when the within subject correlation is 1 or 0 by the previous study, the universal optimal weight was not determined. We modify this asym
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Mistry, Pritesh. "A Knowledge Based Approach of Toxicity Prediction for Drug Formulation. Modelling Drug Vehicle Relationships Using Soft Computing Techniques." Thesis, University of Bradford, 2015. http://hdl.handle.net/10454/14440.

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This multidisciplinary thesis is concerned with the prediction of drug formulations for the reduction of drug toxicity. Both scientific and computational approaches are utilised to make original contributions to the field of predictive toxicology. The first part of this thesis provides a detailed scientific discussion on all aspects of drug formulation and toxicity. Discussions are focused around the principal mechanisms of drug toxicity and how drug toxicity is studied and reported in the literature. Furthermore, a review of the current technologies available for formulating drugs for toxicit
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Glisson, Wesley J., Courtney J. Conway, Christopher P. Nadeau, and Kathi L. Borgmann. "Habitat models to predict wetland bird occupancy influenced by scale, anthropogenic disturbance, and imperfect detection." WILEY, 2017. http://hdl.handle.net/10150/625200.

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Understanding species-habitat relationships for endangered species is critical for their conservation. However, many studies have limited value for conservation because they fail to account for habitat associations at multiple spatial scales, anthropogenic variables, and imperfect detection. We addressed these three limitations by developing models for an endangered wetland bird, Yuma Ridgway's rail (Rallus obsoletus yumanensis), that examined how the spatial scale of environmental variables, inclusion of anthropogenic disturbance variables, and accounting for imperfect detection in validation
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池田, 充., Takeo Ishigaki, Mitsuru Ikeda, 一信 山内, and Kazunobu Yamauchi. "Relationship between Brier score and area under the binormal ROC curve." Elsevier, 2002. http://hdl.handle.net/2237/5310.

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Galadima, Hadiza I. "Controlling for Confounding when Association is Quantified by Area Under the ROC Curve." VCU Scholars Compass, 2015. http://scholarscompass.vcu.edu/etd/3905.

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In the medical literature, there has been an increased interest in evaluating association between exposure and outcomes using nonrandomized observational studies. However, because assignments to exposure are not done randomly in observational studies, comparisons of outcomes between exposed and non-exposed subjects must account for the effect of confounders. Propensity score methods have been widely used to control for confounding, when estimating exposure effect. Previous studies have shown that conditioning on the propensity score results in biased estimation of odds ratio and hazard ratio.
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Liu, Hua. "ASYMPTOTIC PROPERTIES OF PARTIAL AREAS UNDER THE RECEIVER OPERATING CHARACTERISTIC CURVE WITH APPLICATIONS IN MICROARRAY EXPERIMENTS." UKnowledge, 2006. http://uknowledge.uky.edu/gradschool_diss/463.

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Receiver operating characteristic (ROC) curves are widely used in medical decision making. It was recognized in the last decade that only a specific region of the ROC curve is of clinical interest, which can be summarized by the partial area under the ROC curve (partial AUC). Early statistical methods for evaluating partial AUC assume that the data are from a specified underlying distribution. Nonparametric estimators of the partial AUC emerged recently, but there are theoretical issues to be addressed. In this dissertation, we propose two new nonparametric statistics, partially integrated ROC
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Huang, Xin. "Some Topics in Roc Curves Analysis." Digital Archive @ GSU, 2011. http://digitalarchive.gsu.edu/math_diss/3.

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The receiver operating characteristic (ROC) curves is a popular tool for evaluating continuous diagnostic tests. The traditional definition of ROC curves incorporates implicitly the idea of "hard" thresholding, which also results in the empirical curves being step functions. The first topic is to introduce a novel definition of soft ROC curves, which incorporates the idea of "soft" thresholding. The softness of a soft ROC curve is controlled by a regularization parameter that can be selected suitably by a cross-validation procedure. A byproduct of the soft ROC curves is that the corresponding
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Books on the topic "AREA UNDER THE ROC-CURVE (AUC)"

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Lugo, Stefano, and Fabio Bertoni. The Use of Debt by Sovereign Wealth Funds. Edited by Douglas Cumming, Geoffrey Wood, Igor Filatotchev, and Juliane Reinecke. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780198754800.013.6.

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This chapter documents the use of debt capital by sovereign wealth funds (SWFs)—a growing and under-researched phenomenon. Three reasons are given for this. First: debt can help SWFs reach their target portfolio size. (Some do not receive regular inflows from their governments to increase their assets under management (AUM). Second: the development of capital markets is a key objective for most of the countries that have created an SWF, and debt may be especially useful for the development of the bond market. SWF bonds are quasi-governmental securities that can be used as collateral and create
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Schwitter, Juerg. Coronary artery disease. Edited by Dudley Pennell. Oxford University Press, 2018. http://dx.doi.org/10.1093/med/9780198784906.003.0105.

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In the work-up of suspected or known coronary artery disease (CAD), cardiovascular magnetic resonance (CMR) is an established technique and it is recommended by most recent guidelines. Stress dobutamine and stress perfusion CMR yield sensitivities and specificities to detect anatomically defined CAD (&gt;50% coronary stenoses) ranging from 83% to 91% and from 83% to 86%, respectively, with areas under the receiver operating characteristic curve (AUCs) of 0.80–0.93. Multicentre trials report AUCs of 0.75–0.91 to detect CAD and showed superiority over scintigraphic techniques. Increasing evidenc
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Book chapters on the topic "AREA UNDER THE ROC-CURVE (AUC)"

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Abrams, David B., J. Rick Turner, Linda C. Baumann, et al. "Area Under the Curve (AUC)." In Encyclopedia of Behavioral Medicine. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-1005-9_986.

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Nahler, Gerhard. "area under the curve (AUC)." In Dictionary of Pharmaceutical Medicine. Springer Vienna, 2009. http://dx.doi.org/10.1007/978-3-211-89836-9_85.

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Walters, Stephen. "Area Under the Curve (AUC)." In Encyclopedia of Quality of Life and Well-Being Research. Springer Netherlands, 2014. http://dx.doi.org/10.1007/978-94-007-0753-5_107.

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Walters, Stephen. "Area Under the Curve (AUC)." In Encyclopedia of Quality of Life and Well-Being Research. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-17299-1_107.

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Turner, J. Rick. "Area Under the Curve (AUC)." In Encyclopedia of Behavioral Medicine. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-39903-0_986.

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Marcus, Pamela M. "Performance Measures." In Assessment of Cancer Screening. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-94577-0_3.

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AbstractPerformance measures reflect the link between cancer screening test results and cancer diagnoses. They measure the ability of cancer screening to lead to detection of cancer, and provide no evidence as to screening’s ability to reduce mortality. Performance measures are rarely considered sufficient evidence to implement cancer screening for the first time, though they have driven dissemination of tests that are thought to represent upgrades of established cancer screening tests. Chapter 3 presents the six key performance measures: sensitivity, specificity, positive predictive value, ne
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Ishihara, Hideyuki, Fumiaki Oka, Takuma Nishimoto, Masatoshi Yamane, Kazutaka Sugimoto, and Hirokazu Sadahiro. "ADC Threshold Indicating the Ischemic Region for Predicting Efficacy in Thrombectomy." In Acta Neurochirurgica Supplement. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-89844-0_16.

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Abstract Purpose: The effectiveness of endovascular thrombectomy (EVT) has been proven in patients with large cerebral infarction. However, the size of the ischemic region before treatment is a significant factor in the outcome, and the optimal method for the evaluation of this region is uncertain. The goal of this study was to investigate apparent diffusion coefficient (ADC) values as a basis for an assessment of the ischemic region before treatment. Methods: A retrospective study was performed in 48 consecutive patients who underwent EVT for acute large vessel occlusion (LVO) with Alberta St
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Nguyen, Chi Cong, Phuoc Vo, Viet Long Doan, Quang Binh Nguyen, Tien Cuong Nguyen, and Quoc Dinh Nguyen. "Assessment of the Effects of Rainfall Frequency on Landslide Susceptibility Mapping Using AHP Method: A Case Study for a Mountainous Region in Central Vietnam." In Progress in Landslide Research and Technology, Volume 1 Issue 2, 2022. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-18471-0_7.

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AbstractVietnam’s mountainous regions often encounter landslides, frequently resulting in fatalities, infrastructure damage, and landscape destruction. A landslide susceptibility map is an effective tool for mitigating disaster impacts on hazard-prone areas. This study investigates the applicability of the Analytic Hierarchy Process to produce a landslide susceptibility index. Eight major impact factors are analyzed using SAGA, a GIS-based toolkit, including slopes, aspect, land use, soil type, elevation, distance to road, distance to stream, and antecedent rainfall. Four landslide susceptibil
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Ryu, Bikei, Koji Yamaguchi, Tatsuya Ishikawa, et al. "Maximum Nidus Depth as a Risk Factor of Surgical Morbidity in Eloquent Brain Arteriovenous Malformations." In Acta Neurochirurgica Supplement. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-63453-7_14.

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AbstractBackground: Eloquent location of a brain arteriovenous malformation (BAVM) is known to increase the surgical risk. Surgical removal of such BAVMs is challenging. Useful indicators for the safe removal of eloquent BAVMs are needed. The aim of this study was to determine the surgical risk factors for these challenging entities.Methods: The authors retrospectively reviewed 29 motor and/or sensory BAVM patients who underwent surgeries. The risk factors for surgical morbidity were analyzed. As a new risk factor, maximum nidus depth, was evaluated.Results: Complete obliteration was achieved
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Melo, Francisco. "Area under the ROC Curve." In Encyclopedia of Systems Biology. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9863-7_209.

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Conference papers on the topic "AREA UNDER THE ROC-CURVE (AUC)"

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Ermini, Andrea, Riccardo Salvini, and Pier Simone Marrocchesi. "LANDSLIDE SUSCEPTIBILITY MAPPING IN LUNAR SOUTH POLE RIGION." In SGEM International Multidisciplinary Scientific GeoConference 24. STEF92 Technology, 2024. https://doi.org/10.5593/sgem2024/6.1/s28.65.

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The study addresses a challenge in planetary exploration, focusing on landslide susceptibility on the Lunar South Pole Region (LSPR), fundamental for future lunar missions. Features like permanently shadowed regions and water ice deposits attract scientific attention but issues related to rugged terrain and dynamic geological processes must also to be considered. The photointerpretation of data from Lunar Reconnaissance Orbiter (LRO) mission, allowed to create a landslides inventory map. The Frequency Ratio (FR) model was applied to generate a landslide susceptibility map (LSM). Geological and
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Alam, S., O. Olabiyi, O. Odejide, and A. Annamalai. "Energy detector's performance evaluation in a relay based cognitive radio network: Area under the ROC curve (AUC) approach." In 2011 IEEE Globecom Workshops. IEEE, 2011. http://dx.doi.org/10.1109/glocomw.2011.6162466.

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Alam, S., O. Olabiyi, O. Odejide, and A. Annamalai. "A performance study of energy detection for dual-hop transmission with fixed gain relays: area under ROC curve (AUC) approach." In 2011 IEEE 22nd International Symposium on Personal, Indoor and Mobile Radio Communications - (PIMRC 2011). IEEE, 2011. http://dx.doi.org/10.1109/pimrc.2011.6139827.

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Halimu, Chongomweru, Asem Kasem, and S. H. Shah Newaz. "Empirical Comparison of Area under ROC curve (AUC) and Mathew Correlation Coefficient (MCC) for Evaluating Machine Learning Algorithms on Imbalanced Datasets for Binary Classification." In the 3rd International Conference. ACM Press, 2019. http://dx.doi.org/10.1145/3310986.3311023.

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Gantert, Luana, and Miguel Elias M. Campista. "Seleção de Modelo de Aprendizado Federado Baseado em Busca e Poda para Detecção de Defeitos Industriais." In Simpósio Brasileiro de Redes de Computadores e Sistemas Distribuídos. Sociedade Brasileira de Computação, 2024. http://dx.doi.org/10.5753/sbrc.2024.1548.

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O aprendizado federado surge como uma alternativa ao aprendizado de máquina tradicional ao descentralizar o treinamento dos modelos. Os dispositivos clientes se comunicam com um servidor central e treinam o modelo definido de maneira iterativa. A definição antecipada do modelo a ser treinado, porém, é um desafio pouco discutido. Este trabalho propõe a seleção da melhor rede neural a partir de um procedimento de busca envolvendo múltiplas redes, e posterior poda daquelas que se mostrarem menos promissoras durante o treinamento. Para isso, em uma dada rodada de avaliação, os nós enviam os result
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De Melo, Matheus, Andy Gajadhar, Hugo De Oliveira, Arnaldo De Andrade e Silva, and Leonardo Batista. "Analysis of Shape-Based and Texture-Based Attributes in Classification of Mammographic Findings by Machine Learning Algorithms." In ncipais do Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2015. http://dx.doi.org/10.5753/sbcas.2015.10364.

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Breast cancer is the most frequent cancer type among women. We present a method of classification of nodules (malignant or benign) found in mammograms using shape-based attributes and texture-based ones. Firstly, we built a test database, then we segmented and extracted a Gray Level Cooccurrence Matrix (GLCM) from each mammographic finding and analyzed texture-based and shape-based attributes. Finally, classification was performed through machine learning algorithms. Tests reached a maximum Correct Classification Rate (CCR) of 93.75%, when performed with the Radial Basis Function Network algor
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Khan, Shahzad Ali, and Zeeshan Ali Rana. "Evaluating Performance of Software Defect Prediction Models Using Area Under Precision-Recall Curve (AUC-PR)." In 2019 2nd International Conference on Advancements in Computational Sciences (ICACS). IEEE, 2019. http://dx.doi.org/10.23919/icacs.2019.8689135.

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Amagada, P. U. "An Inferable Machine Learning Approach for Reservoir Lithology Characterization Using Drilling Data." In SPE Annual Technical Conference and Exhibition. SPE, 2023. http://dx.doi.org/10.2118/217485-stu.

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Abstract Reservoir lithology is a key factor in petroleum exploration and petrophysical calculations. It is of utmost importance as it serves as a foundation for reservoir characterization and formation evaluation. Accurate estimation of the reservoir permeability, porosity, and water saturation, is greatly dependent on accurate identification of the reservoir lithology. Ideally, the reservoir lithology is determined by obtaining physical samples of the reservoir. This process is however very expensive and time-consuming, hence the wide adoption of well log responses for identifying the reserv
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BuKhamseen, N. Y., M. Maucec, A. Aneddame, and H. H. Mohammadi. "Machine Learning-Enabled Classification System to Screen and Rank Horizontal Wells by Sidetracking Success Criteria." In International Petroleum Technology Conference. IPTC, 2024. http://dx.doi.org/10.2523/iptc-24355-ea.

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Abstract Sidetracking vertical wellbores into horizontal laterals to avoid or delay water coning is a common practice in managing oil reservoirs under water flooding. Accurately predicting if the sidetrack would improve the performance of the well (i.e., reduce water production, increase oil production, or both) represents a vital information to decide whether it is optimal to sidetrack or continue with current vertical hole and withstand the increasing water cut. In this paper, we employ algorithms of predictive analytics and machine learning (ML) on a large synthetic but realistic data set o
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Figueirêdo, Ilan Sousa, Tássio Farias Carvalho, Wenisten José Dantas Silva, Lílian Lefol Nani Guarieiro, and Erick Giovani Sperandio Nascimento. "Detecting Interesting and Anomalous Patterns In Multivariate Time-Series Data in an Offshore Platform Using Unsupervised Learning." In Offshore Technology Conference. OTC, 2021. http://dx.doi.org/10.4043/31297-ms.

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Abstract Detection of anomalous events in practical operation of oil and gas (O&amp;G) wells and lines can help to avoid production losses, environmental disasters, and human fatalities, besides decreasing maintenance costs. Supervised machine learning algorithms have been successful to detect, diagnose, and forecast anomalous events in O&amp;G industry. Nevertheless, these algorithms need a large quantity of annotated dataset and labelling data in real world scenarios is typically unfeasible because of exhaustive work of experts. Therefore, as unsupervised machine learning does not require an
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Reports on the topic "AREA UNDER THE ROC-CURVE (AUC)"

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Chiu, Chia-Yu, and Amara Sarwal. Impact of area under the curve-based vancomycin dosing combination with anti-pseudomonal beta-lactam antibiotics: a systematic review and meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2022. http://dx.doi.org/10.37766/inplasy2022.12.0025.

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Review question / Objective: Did AUC-based vancomycin dosing reduce acute kidney injury than trough-based dosing when combined with anti-pseudomonal beta-lactam antibiotic? Condition being studied: Patients received Vancomycin combined with anti-pseudomonal beta-lactam antibiotics and monitor with either trough-base dosing or AUC-based dosing vancomycin. Information sources: All study types except case reports, case series, and conference abstracts were considered. PubMed, Embase, Cochrane Library, and ClinicalTrials.gov were searched from inception to November2022.
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Tayeb, Shahab. Taming the Data in the Internet of Vehicles. Mineta Transportation Institute, 2022. http://dx.doi.org/10.31979/mti.2022.2014.

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As an emerging field, the Internet of Vehicles (IoV) has a myriad of security vulnerabilities that must be addressed to protect system integrity. To stay ahead of novel attacks, cybersecurity professionals are developing new software and systems using machine learning techniques. Neural network architectures improve such systems, including Intrusion Detection System (IDSs), by implementing anomaly detection, which differentiates benign data packets from malicious ones. For an IDS to best predict anomalies, the model is trained on data that is typically pre-processed through normalization and f
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Jittapiromsak, Nutchawan. Value of Dynamic Contrast-Enhanced Magnetic Resonance Imaging for Determining the Plasma Epstein-Barr Virus Status and Staging of Nasopharyngeal Carcinoma. Faculty of Medicine, Chulalongkorn University, 2019. https://doi.org/10.58837/chula.res.2019.9.

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Objective: To determine the associations between dynamic-contrast enhanced (DCE) magnetic resonance imaging (MRI) parameters and plasma Epstein-Barr virus (EBV) DNA status and nasopharyngeal carcinoma (NPC) stages. Methods: We prospectively studied the DCE MRI results of 47 patients with newly diagnosed NPC and a known pre-treatment plasma EBV DNA level. Regions of interest (ROIs) were drawn at primary tumors, and DCE MRI parameters, including mean and max values of Ktrans, Kep Ve, and Vp, were recorded. Spearman’s rank correlation was used to identify significant associations between DCE MRI
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Alwan, Iktimal, Dennis D. Spencer, and Rafeed Alkawadri. Comparison of Machine Learning Algorithms in Sensorimotor Functional Mapping. Progress in Neurobiology, 2023. http://dx.doi.org/10.60124/j.pneuro.2023.30.03.

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Objective: To compare the performance of popular machine learning algorithms (ML) in mapping the sensorimotor cortex (SM) and identifying the anterior lip of the central sulcus (CS). Methods: We evaluated support vector machines (SVMs), random forest (RF), decision trees (DT), single layer perceptron (SLP), and multilayer perceptron (MLP) against standard logistic regression (LR) to identify the SM cortex employing validated features from six-minute of NREM sleep icEEG data and applying standard common hyperparameters and 10-fold cross-validation. Each algorithm was tested using vetted feature
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DSC-MRI Consensus QIBA Profile. Chair Ona Wu, Mark Shiroishi, and Leland Hu. Radiological Society of North America (RSNA)/Quantitative Imaging Biomarkers Alliance (QIBA), 2020. https://doi.org/10.1148/qiba/20201022.

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The goal of a QIBA Profile is to help achieve a useful level of performance for a given biomarker. Profile development is an evolutionary, phased process; this Profile is in the Public Comment Resolution Draft stage. The performance claims represent expert consensus and will be empirically demonstrated at a subsequent stage. Users of this Profile are encouraged to refer to the following site to understand the document’s context: http://qibawiki.rsna.org/index.php/QIBA_Profile_Stages. The Claim (Section 2) describes the biomarker performance. The Activities (Section 3) contribute to generating
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Pang, Jinji. Nonparametric bootstrap methods for interval estimation of the area under the ROC curve for correlated diagnostic test data. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-1064.

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Wu, Bin, Lixia Guo, Kaikai Zhen, and Chao Sun. Diagnostic and prognostic value of miRNAs in hepatoblastoma: A systematic review with meta-analysis. INPLASY - International Platform of Registered Systematic Review and Meta-analysis Protocols, 2021. http://dx.doi.org/10.37766/inplasy2021.11.0045.

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Review question / Objective: Background and aim: Increasing evidence has revealed the valuable diagnostic and prognostic applications of dysregulated microRNAs (miRNAs) in hepatoblastoma (HB), the most common hepatic malignancy during childhood. However, these results are inconsistent and remain to be elucidated. In the present study, we aimed to systematically compile up-to-date information regarding the clinical value of miRNAs in HB. Methods: Articles concerning the diagnostic and prognostic value of single miRNAs for HB were searched from databases. The sensitivity (SEN), specificity (SPE)
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Kahwati, Leila, Matthew Avenarius, Leslie Brouwer, et al. Blood-Based Tests for Multiple Cancer Screening: A Systematic Review. AHRQ, 2025. https://doi.org/10.23970/ahrqepcsrmultiple.

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Objectives. Screening for multiple cancers in a single blood test is potentially transformative. The objective of this review was to assess the benefits, harms, and accuracy of screening with blood-based multicancer screening tests (MCST) in asymptomatic adults. Data sources. Medline, Cochrane Library, trial registries, relevant government and commercial websites through December 2024; surveillance was conducted through March 31, 2025. Study Selection. Eligible designs included controlled studies for benefit outcomes (e.g., cancer mortality, cancer detection, quality of life), controlled and u
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Nova Martínez, Manuel Arturo, Elkin Giovani Sorza Álvarez, and Lina María Zabala Arango. Defined integration and applications for measuring areas and distances. Ediciones Universidad Cooperativa de Colombia, 2024. https://doi.org/10.16925/gcnc.103.

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Among the most relevant applications of the definite integral in functions of a real variable are the computation of areas in non-regular flat regions and measuring distances along curved paths, which are widely employed in engineering and physics. These notes present the concept of definite integration, emphasizing its connection to calculating the area under a curve, as well as the arc length formula and its practical application for length measurement. Problem scenarios are introduced to illustrate the utility of these concepts, aiming to provide a solid understanding of the topics and thei
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Hamlin, Alexandra, Erik Kobylarz, James Lever, Susan Taylor, and Laura Ray. Assessing the feasibility of detecting epileptic seizures using non-cerebral sensor. Engineer Research and Development Center (U.S.), 2021. http://dx.doi.org/10.21079/11681/42562.

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This paper investigates the feasibility of using non-cerebral, time-series data to detect epileptic seizures. Data were recorded from fifteen patients (7 male, 5 female, 3 not noted, mean age 36.17 yrs), five of whom had a total of seven seizures. Patients were monitored in an inpatient setting using standard video electroencephalography (vEEG), while also wearing sensors monitoring electrocardiography, electrodermal activity, electromyography, accelerometry, and audio signals (vocalizations). A systematic and detailed study was conducted to identify the sensors and the features derived from t
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