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

ABSTRACT:

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 nomog

SUBMITTER: Zhang R 

PROVIDER: S-EPMC12007213 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature