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In this paper, we constructed a risk model to simulate the clinical information available to the treating clinician and then statistically measured the predictive benefit of adding the information from plasma HBP through cfNRI, IDI and improvement in ROC area.
Processing the full set of 280 files with D-MIAT took a total of approximately 15 min. We then measured the predictive accuracy of 7 different classification algorithms: Naive Bayes, SVM using the RBF kernel, K-nn using the value of k=3, AdaBoost, C4.5, Random Forest, and Logistic regression on each of these datasets.
Therefore, we assessed the acute responses of serum GH levels to a new octreotide test (intravenous administration of 50 μg) in 98 consecutive patients with active acromegaly and we measured the predictive value of this test for the efficacy of chronic octreotide-long acting repeatable (octreotide-LAR) treatment in 18 patients.
A systematic search was conducted to identify studies that measured the predictive validity of the nine instruments (Fig. 1).
A systematic search was conducted to identify studies that measured the predictive validity of the nine tools.
We excluded studies if they measured the predictive validity of select scales of a measure, if instruments were coded retrospectively without blinding to outcomes, or if they were calibration studies for the actuarial tools (which may give inflated effects).
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MCC = TP × TN - FP × FN (TP + FP ) (TP + FN ) (TN + FP ) (TN + FN ) An MCC-robustness value is used to measure the predictive power and its stability of a classifier, a measurement index for top probes' selection in the MFS.
AIC, defined as deviance plus 2 times the number of predictors, measures the predictive power; a model is estimated to reduce out-of-sample prediction error if AIC decreases.
To improve clinical utility of significant continuous variables, Youden's index was used to calculate the best cutoff value for predicting hypocalcemia.[ 15] The AUC was used to measure the predictive accuracy.
Frank Schmidt, a business professor at the University of Iowa, analysed a century of workplace productivity data to measure the predictive value of various selection processes.
We measure the predictive power of a number of contextual features (e.g. social proximity, time, call/SMS).
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