area_under_the_receiver_operating_characteristic

Area under the receiver operating characteristic

A receiver operating characteristic curve, i.e., ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied.

In a ROC curve the true positive rate (Sensitivity) is plotted in function of the false positive rate (100-Specificity) for different cut-off points of a parameter. … The area under the ROC curve ( AUC ) is a measure of how well a parameter can distinguish between two diagnostic groups (diseased/normal).


In a ROC curve the true positive rate (Sensitivity) is plotted in function of the false positive rate (100-Specificity) for different cut-off points of a parameter. … The area under the ROC curve ( AUC ) is a measure of how well a parameter can distinguish between two diagnostic groups (diseased/normal).


Intraoperative neurophysiological monitoring using somatosensory evoked potentials has been linked to a reduction in the incidence of neurological deficits during corrective surgery. Nonetheless, quantitative assessments of somatosensory evoked potential waveforms are often difficult to evaluate, because they are affected by anesthesia, injury, and noise.

Jorge et al., discuss a novel method that integrates somatosensory evoked potential signals into a single metric by calculating the area under the curve (AUC).

Thirty-two Sprague Dawley rats underwent a laminectomy procedure and were then randomly assigned to a control group or to receive a contusive spinal cord injury ranging from 100 to 200 kilodynes. Neurophysiological testing was completed at various points perioperatively and postoperatively. Somatosensory evoked potential traces obtained were processed and the AUC metric was calculated.

The AUC significantly decreased to 11% of its baseline value after impact and remained at 25% baseline after 1 hour for the 200-kdyn cohort. Postimpact, AUC for the control versus the 150-kdyn and 200-kdyn groups, and the 150-kdyn versus 200-kdyn groups were significantly higher (P < 0.01, P < 0.001, and P < 0.05, respectively). Across days, the only significant parameter accounting for AUC variability was impact force, P < 0.0001 (subject parameters and number of days were not significant).

The AUC metric can detect an iatrogenic contusive spinal cord injury immediately after its occurrence. Moreover, this metric can detect different iatrogenic injury impact force levels and identify injury in the postoperative period. The AUC integrates multiple Intraoperative neurophysiological monitoring measures into a single metric and thus has the potential to help clinicians and investigators evaluate spinal cord impact injury status 1).


The purpose of this study was to build a model of machine learning (ML) for the prediction of mortality in patients with isolated moderate and severe traumatic brain injury (TBI).

Hospitalized adult patients registered in the Trauma Registry System between January 2009 and December 2015 were enrolled in this study. Only patients with an Abbreviated Injury Scale (AIS) score ≥ 3 points related to head injuries were included in this study. A total of 1734 (1564 survival and 170 non-survival) and 325 (293 survival and 32 non-survival) patients were included in the training and test sets, respectively.

Using demographics and injury characteristics, as well as patient laboratory data, predictive tools (e.g., logistic regression [LR], support vector machine [SVM], decision tree [DT], naive Bayes [NB], and artificial neural networks [ANN]) were used to determine the mortality of individual patients. The predictive performance was evaluated by accuracy, sensitivity, and specificity, as well as by area under the curve (AUC) measures of receiver operator characteristic curves. In the training set, all five ML models had a specificity of more than 90% and all ML models (except the NB) achieved an accuracy of more than 90%. Among them, the ANN had the highest sensitivity (80.59%) in mortality prediction. Regarding performance, the ANN had the highest AUC (0.968), followed by the LR (0.942), SVM (0.935), NB (0.908), and DT (0.872). In the test set, the ANN had the highest sensitivity (84.38%) in mortality prediction, followed by the SVM (65.63%), LR (59.38%), NB (59.38%), and DT (43.75%).

The ANN model provided the best prediction of mortality for patients with isolated moderate and severe TBI 2).


1)
Jorge A, Zhou J, Dixon EC, Hamilton KD, Balzer J, Thirumala P. Area Under the Curve of Somatosensory Evoked Potentials Detects Spinal Cord Injury. J Clin Neurophysiol. 2019 Jan 28. doi: 10.1097/WNP.0000000000000563. [Epub ahead of print] PubMed PMID: 30694945.
2)
Rau CS, Kuo PJ, Chien PC, Huang CY, Hsieh HY, Hsieh CH. Mortality prediction in patients with isolated moderate and severe traumatic brain injury using machine learning models. PLoS One. 2018 Nov 9;13(11):e0207192. doi: 10.1371/journal.pone.0207192. eCollection 2018. PubMed PMID: 30412613.
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  • Last modified: 2019/02/19 22:32
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