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We provide an in-depth analysis methodology for non-uniform conditions, demonstrating the necessity of 3D models to correctly characterize non-uniformity and leveraging the synergy between 1D and 3D models.
Finally, AUCROC refers to the Area Under the Receiver Operating Characteristic curve and provides information about the ability of the models to correctly classify users with or without flu-like and cold symptoms.
We compared the ability of the 2 models to correctly allocate patients.
Future risk factor studies in the Agincourt subdistrict will employ Bayesian geostatistical models to correctly quantify risk factors for mortality by age group.
A receiver operating characteristic (ROC) analysis was conducted to evaluate the ability of the two models to correctly discriminate between subjects of low and elevated HOMA-IR.
The ability of the models to correctly classify patients was summarised by their 'area under the receiver-operating-characteristic curve' (AUROC) [ 28], along with their sensitivity, specificity, positive predictive value and negative predictive value.
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AUC measures the discriminating accuracy of the (linear or non linear) model, i.e., the ability of the model to correctly classify patients in the positive or in the negative group.
Reliability is defined in the present framework as the capacity of a model to correctly project the health workforce deemed to be adequate at some identified future time.
Measures of discrimination Discrimination is the ability of the prediction model to correctly separate individuals into those who will and those who will not have the event of interest.
Results demonstrate the ability of the model to correctly reproduce the location and magnitude of soil loss and accumulation.
The ability of the calibrated model to correctly predict the long-run welfare changes induced by a set of policy experiments is assessed.
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