(A) H measure at different severity ratios. Severity ratio is the ratio of the cost of false positive over the cost of false negative predictions.; (B) Table of H measure, AUC, true positive (TP), false positive (FP), true negative (TN) and false negative (FN) predictions of scores.</p
<p>Comparison of the AUROC values between the inflammation-based scores and the staging systems.</p
<p>Comparison of subscale scores and summary scores using different scoring methods, by medication s...
<p>TP, FP and FN for each algorithm. Note that each plot has a different scale, which is very low in...
<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hi...
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorith...
<p>For each predictor (classifier), the table reports the number of true positives (TP), false posit...
<p>Comparison of prediction performance of classifiers in terms of F2 score, at different levels hie...
In this scenario, Ground Truth positive patients and Ground Truth negative patients are equally like...
Comparison of mean reference ELISA index values (IVs) between individuals who had true positive vs. ...
<p>Weighted classification score for the full range of thresholds using different trade-offs between...
Comparison of Adjusted Rand Index (ARI) scores of different combinations of our proposed approach.</...
Summary of results for multiple feature types on optimal weighted F scores. (FNR = False Negative Ra...
Comparison of Normalized Mutual Information (NMI) scores of different combinations of our proposed a...
<p>The metrics are calculated after combining the responses of all the experts using EM and then ass...
Comparison of the averaged HRs (over 5 trials and 20 subjects) of positive and negative displays for...
<p>Comparison of the AUROC values between the inflammation-based scores and the staging systems.</p
<p>Comparison of subscale scores and summary scores using different scoring methods, by medication s...
<p>TP, FP and FN for each algorithm. Note that each plot has a different scale, which is very low in...
<p>Comparison of prediction performance of classifiers in terms of AUC score, at different levels hi...
Comparison of false positive ratio (FPR) and true positive ratio (TPR) for machine learning algorith...
<p>For each predictor (classifier), the table reports the number of true positives (TP), false posit...
<p>Comparison of prediction performance of classifiers in terms of F2 score, at different levels hie...
In this scenario, Ground Truth positive patients and Ground Truth negative patients are equally like...
Comparison of mean reference ELISA index values (IVs) between individuals who had true positive vs. ...
<p>Weighted classification score for the full range of thresholds using different trade-offs between...
Comparison of Adjusted Rand Index (ARI) scores of different combinations of our proposed approach.</...
Summary of results for multiple feature types on optimal weighted F scores. (FNR = False Negative Ra...
Comparison of Normalized Mutual Information (NMI) scores of different combinations of our proposed a...
<p>The metrics are calculated after combining the responses of all the experts using EM and then ass...
Comparison of the averaged HRs (over 5 trials and 20 subjects) of positive and negative displays for...
<p>Comparison of the AUROC values between the inflammation-based scores and the staging systems.</p
<p>Comparison of subscale scores and summary scores using different scoring methods, by medication s...
<p>TP, FP and FN for each algorithm. Note that each plot has a different scale, which is very low in...