The Experts below are selected from a list of 263979 Experts worldwide ranked by ideXlab platform
Cecile A J W Janssens - One of the best experts on this subject based on the ideXlab platform.
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small improvement in the area under the receiver operating characteristic curve indicated small changes in predicted risks
Journal of Clinical Epidemiology, 2016Co-Authors: Forike K Martens, Elisa C M Tonk, Jannigje G Kers, Cecile A J W JanssensAbstract:Abstract Objective Adding risk factors to a prediction Model often increases the area under the receiver operating characteristic curve (AUC) only slightly, particularly when the AUC of the Model was already high. We investigated whether a risk factor that minimally improves the AUC may nevertheless improve the predictive ability of the Model, assessed by integrated discrimination improvement (IDI). Study Design and Setting We simulated data sets with risk factors and event status for 100,000 hypothetical individuals and created prediction Models with AUCs between 0.50 and 0.95. We added a single risk factor for which the effect was Modeled as a certain odds ratio (OR 2, 4, 8) or AUC increment (ΔAUC 0.01, 0.02, 0.03). Results Across all AUC values of the Baseline Model, for a risk factor with the same OR, both ΔAUC and IDI were lower when the AUC of the Baseline Model was higher. When the increment in AUC was small (ΔAUC 0.01), the IDI was also small, except when the AUC of the Baseline Model was >0.90. Conclusion When the addition of a risk factor shows minimal improvement in AUC, predicted risks generally show minimal changes too. Updating risk Models with strong risk factors may be informative for a subgroup of individuals, but not at the population level. The AUC may not be as insensitive as is frequently argued.
Ziqiang Lang - One of the best experts on this subject based on the ideXlab platform.
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Baseline Model based structural health monitoring method under varying environment
Renewable Energy, 2019Co-Authors: Xueyan Zhao, Ziqiang LangAbstract:Environment has significant impacts on the structure performance and will change features of sensor measurements on the monitored structure. The effect of varying environment needs to be considered and eliminated while conducting structural health monitoring. In order to achieve this purpose, a Baseline Model based structural health monitoring method is proposed in this paper. The relationship between signal features and varying environment, known as a Baseline Model, is first established. Then, a tolerance range of the signal feature is evaluated via a data based statistical analysis. Furthermore, the health indicator, which is defined as the proportion of signal features within the tolerance range, is used to judge whether the structural system is in normal working condition or not so as to implement the structural health monitoring. Finally, experimental data analysis for an operating wind turbine is conducted and the results demonstrate the performance of the proposed new technique.
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A novel health probability for structural health monitoring
2012Co-Authors: Xueyan Zhao, Ziqiang LangAbstract:A novel structural health monitoring strategy is proposed in this paper. A Baseline Model between the operating parameters and measurement features is established firstly to generate the Baseline working feature and validated. Then a tolerance range of deviation of practical working features from the Baseline Model is computed based on normal distribution. Furthermore, health probability is defined as the proportion of the number of working status in the corresponding tolerance range. Finally, the effectiveness of such novel structural health monitoring strategy is validated by simulation study and experimental work.
Andrea Tafuro - One of the best experts on this subject based on the ideXlab platform.
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The Effects of Uncertainty Shocks on Daily Prices
Journal of Business Cycle Research, 2018Co-Authors: Dario Bonciani, Andrea TafuroAbstract:In this paper, we investigate the effects of uncertainty shocks on the US daily online price index by Cavallo and Rigobon (J Econ Perspect 30(2):151–78, 2016 ) within a VAR framework. We find evidence that shocks increasing uncertainty dampen prices significantly. This result is robust to various changes to the Baseline Model and rejects the Upward Pricing Bias that is often found in the Sticky-Price DSGE literature.
Fiona Lecky - One of the best experts on this subject based on the ideXlab platform.
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comparing Model performance for survival prediction using total glasgow coma scale and its components in traumatic brain injury
Journal of Neurotrauma, 2013Co-Authors: Mehdi Moazzez Lesko, T Jenks, Sara J Obrien, Charmaine Childs, Omar Bouamra, M Woodford, Fiona LeckyAbstract:Abstract The Glasgow Coma Scale (GCS) score is used in clinical practice for patient assessment and communication among clinicians and also in outcome prediction Models such as the Trauma and Injury Severity Score (TRIS). The objective of this study is to determine which GCS subscore is best associated with outcome, taking time of assessment into account. Records of patients with brain injury who presented after 1989 were extracted from the Trauma Audit and Research Network (TARN) database. Using logistic regression, a Baseline Model was derived with age, Injury Severity Score (ISS), and year of injury as covariates and survival at discharge as the dependent variable. Total GCS, its subscores, and their combinations at various time points were separately added to the Baseline Model to compare their effect on Model performance. The dataset contained 21,657 cases. The total GCS score at scene and its subscores had significantly lower predictive power compared with those recorded on arrival at the Emergency ...
Mark R Wiesner - One of the best experts on this subject based on the ideXlab platform.
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the use of bayesian networks for nanoparticle risk forecasting Model formulation and Baseline evaluation
Science of The Total Environment, 2012Co-Authors: Eric S Money, Kenneth H Reckhow, Mark R WiesnerAbstract:We describe the use of Bayesian networks as a tool for nanomaterial risk forecasting and develop a Baseline probabilistic Model that incorporates nanoparticle specific characteristics and environmental parameters, along with elements of exposure potential, hazard, and risk related to nanomaterials. The Baseline Model, FINE (Forecasting the Impacts of Nanomaterials in the Environment), was developed using expert elicitation techniques. The Bayesian nature of FINE allows for updating as new data become available, a critical feature for forecasting risk in the context of nanomaterials. The specific case of silver nanoparticles (AgNPs) in aquatic environments is presented here (FINEAgNP). The results of this study show that Bayesian networks provide a robust method for formally incorporating expert judgments into a probabilistic measure of exposure and risk to nanoparticles, particularly when other knowledge bases may be lacking. The Model is easily adapted and updated as additional experimental data and other information on nanoparticle behavior in the environment become available. The Baseline Model suggests that, within the bounds of uncertainty as currently quantified, nanosilver may pose the greatest potential risk as these particles accumulate in aquatic sediments.