Your privacy, your choice

We use essential cookies to make sure the site can function. We also use optional cookies for advertising, personalisation of content, usage analysis, and social media.

By accepting optional cookies, you consent to the processing of your personal data - including transfers to third parties. Some third parties are outside of the European Economic Area, with varying standards of data protection.

See our privacy policy for more information on the use of your personal data.

for further information and to change your choices.

Skip to main content
Fig. 1 | BMC Pulmonary Medicine

Fig. 1

From: A clinical data-driven machine learning approach for predicting the effectiveness of piperacillin-tazobactam in treating lower respiratory tract infections

Fig. 1

Workflow of the research. IC, inclusion criteria; EC, exclusion criteria; RUS, the Random Under-Sampling; RFE, Recursive Feature Elimination; LR, logistic regression; RF, random forest; SVM, Support Vector Machine; GNB, Gaussian Naive Bayes; FN, false negative; TN, true negative; TP, true positive; FP, false positive

Back to article page