Sentence examples for decision sensitivity from inspiring English sources

For ROC analysis of the recognition of candidate A as a positive candidate against candidate C, the deviations of the preset cutoffs from the mean of the starting material were divided by the SD of the starting material for plotting against 1-specificty to observe the association of decision sensitivity with the preset cutoffs (Figure 4).

If a clinician prefers to decrease the number of false-positive decisions, sensitivity needs to be high, and a threshold of 0.5 or higher may be considered resulting in a sensitivity of at least 0.95.

We will examine the validity of this decision in sensitivity analyses.

Of 46 EID events of international public health concern, 44 would have been reported by using the Annex 2 decision instrument (sensitivity 95.6%; 95% CI 89.8%100%%).

In this study, we defined a simple, practical, relatively accurate clinical decision rule (sensitivity and specificity of 68%) that can be applied at the bedside [ 32] to predict recurrent shock in children hospitalized for a first dengue shock episode.

Including FDG-PET in the evaluation of patients with recurrent colorectal carcinoma has been shown to be cost-effective in a study using clinical evaluation of effectiveness with modeling of costs and studies using decision tree sensitivity analysis [ 22, 93– 93].

It is also involved in decision-making, sensitivity to reward, memory, and predicting the future.

We show how SHEPhERd works on a smartphone mobile application example, and we highlight its potential to drive architectural decisions through sensitivity analysis.

The MCDM applied are particularly effective at providing insight to decisions when sensitivity analysis is conducted based upon different weights (i.e., strategic priorities) applied to the key technical attributes of the well completion alternatives.

By applying fractional factorial methods to the GCAM-USA 50-state integrated-assessment model in the context of a particular policy question, this paper demonstrates how a decision-focused sensitivity analysis strategy can greatly reduce computational burden in the presence of uncertainty and reveal the important drivers for decisions and more detailed uncertainty analysis.

Reported experience and modelling approaches, such as decision-tree sensitivity analysis, can demonstrate that these management changes translate to improved cost-effectiveness (Fig. 3).