Automated machine learning for early prediction of systemic inflammatory response syndrome in acute pancreatitis.

Background: Systemic inflammatory response syndrome (SIRS) is a frequent and serious complication of acute pancreatitis (AP), often associated with increased mortality. This study aims to leverage automated machine learning (AutoML) algorithms to create a model for the early and precise prediction of SIRS in AP.

Methods: This study retrospectively analyzed patients diagnosed with AP across multiple centers from January 2017 to December 2021. Data from the First Affiliated Hospital of Soochow University and Changshu Hospital were used for training and internal validation, while testing was conducted with data from the Second Affiliated Hospital. Predictive models were constructed and validated using the least absolute shrinkage and selection operator (LASSO) and AutoML. A nomogram was developed based on multivariable logistic regression (LR) analysis, and the performance of the models was assessed through receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). Additionally, the AutoML model's effectiveness and interpretability were assessed through DCA, feature importance, SHapley Additive exPlanation (SHAP) plots, and locally interpretable model-agnostic explanations (LIME).

Results: A total of 1,224 patients were included, with 812 in the training cohort, 200 in validation, and 212 in testing. SIRS occurred in 33.7% of the training cohort, 34.0% in validation, and 22.2% in testing. AutoML models outperformed traditional LR, with the deep learning (DL) model achieving an area under the ROC curve of 0.843 in the training set, and 0.848 and 0.867 in validation and testing, respectively.

Conclusion: The AutoML model using the DL algorithm is clinically significant for the early prediction of SIRS in AP.