Fig 1.
Patient selection process.
Fig 2.
Classification pipeline.
Table 1.
Characteristics of patients and pleural fluid samples by diagnosis.
Fig 3.
Receiver operating characteristic curves using validation predictions.
The dots correspond to the points that maximize the Youden index.
Fig 4.
Precision-recall curves using validation predictions.
Precision-recall curve for each method using the validation predictions. The dots correspond to the points that maximize the F1 score.
Table 2.
Accuracy (Acc), sensitivity (SEN), specificity (SPF) and F1 score (F1) of all the classifiers in the validation stage, using three different thresholds (T): 0.5, the one that maximizes the Youden index on the receiver operating characteristic curve and the one that maximizes the F1 score on the precision-recall curve, and their confidence intervals at 95% level.
Table 3.
Threshold (T), area under the curve (AUC), accuracy (Acc), sensitivity (SEN), specificity(SPF) and F1 score (F1) of all the classifiers in the test stage, using the best thresholds found in the validation stage with their confidence intervals at 95% level.
Fig 5.
Post-test probability of TPE after positive (top) or negative (bottom) results of 1) ADA>40 U/l plus implicit lymphocyte percentage >50% alone or 2) in addition to age and routine pleural fluid parameters included in the machine learning algorithms; for different pre-test probabilities of disease.
Table 4.
Comparative table of Bayesian probabilities of test parameters used: ADA > 40 U/l (plus lymphocyte percentage > 50%) versus the whole set of variables included in the machine learning algorithms.
PPV: positive predictive value. NPV: negative predictive value.