Development of machine learning models for predicting acute respiratory distress syndrome：evidence from the MIMIC-III and MIMIC-IV

Abstract Background Acute Respiratory Distress Syndrome (ARDS) is a prevalent condition in the ICU with a mortality rate of 27% to 45%. Despite the Berlin definition being the current diagnostic standard, it has significant limitations. This study aims to establish and validate a novel machine learning-based prediction model for ARDS in ICU patients. Methods The data of suspected ARDS patients was extracted from the Medical Information Mart for Intensive Care (MIMIC)-III and MIMIC-IV databases. Ten-fold cross-validation was employed, utilizing machine learning algorithms such as K-nearest neighbor (KNN), support vector machine (SVM), Decision Tree Classifier (DTC), random forest (RF), extreme gradient boosting (XGBoost), light gradient boosting (LightGB), and categorical boosting (CatBoost) and logistic regression (LR) for model construction. Finally, the performance of these models was evaluated based on metrics including area under the ROC curve, calibration curve, and clinical decision curve. Results A total of 2,852 patients who met the exclusion criteria were included in the final study, of which 2078 patients developed ARDS.We established scoring models, such as LR, KNN, SVM, DTC, RF, XGBoost, LightGB, and CatBoost. The area under the receiver operating characteristic curve (AUC) values for each model were as follows: LR - 0.664, KNN - 0.692, SVM - 0.567, DTC - 0.709, RF - 0.732, XGBoost - 0.793, LightGB - 0.793, and CatBoost - 0.817. Notably, CatBoost exhibited superior predictive performance in discrimination, calibration, and clinical applicability compared to all other models. Conclusions The application of machine learning models has showcased their robustness in predicting ARDS. Notably, the CatBoost algorithm emerges as the most promising in terms of predictive performance.