https://www.selleckchem.com/products/bay-218.html
Primary outcomes compared consisted of predictive model area under curve of receiver-operator characteristic curve (AUC), and macro-averaged F1 score. Secondary outcomes included sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). Deep neural network yielded an AUC of 0.761 (95% CI 0.725-0.797) and F1 score of 0.661 (95% CI 0.633-0.689), which was superior to logistic regression (AUC 0.667 (95% CI 0.627-0.707), F1 score 0.596 (95% CI 0.567-0.625)). Deep neural network had a specificity of 91.5
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