Fig. 1
The overall design of the study. (a) Retrospective collection of baseline clinical information as well as time-series chest radiographs and laboratory indicators. Quantitative assessment of chest radiographs abnormalities, specifically through the quantitative scoring of chest radiographs by radiologists, aiming to detect and diagnose pneumonia more accurately. (b) We constructed four nested logistic regression models for classifying pulmonary infection and colonization of A. baumannii combined difference clinical characteristic. (c) Model performance was assessed using AUC, decision curve analysis and calibration curve. (d) We adopted Shapley additive explanation (SHAP) values to determine which features contributed most to model predictions on the logistic regression predictions. T1: within 1 day of admission; T2, 3 days before culturing out the strain; T3, 1 day within culturing out the strain. 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. ROC: receiver operating characteristic curves. DCA: decision curve analysis