Fig. 2From: Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumanniiReceiver operating characteristic curves of the four models to classify pulmonary A. baumannii colonization and infection. Model 1, clinical baseline information + laboratory indicators and radiographic features of T3. Model 2, model1 + the change value of between T3 and T1. Model 3, model 1 + the change value of between T3 and T2. Model 4, model 2 + model 3. All statistical comparisons between area under the receiver operating characteristic curve values of models 1–4 were significant (p < 0.05)Back to article page