Development and validation of a machine learning-based nomogram model integrating LASSO regression and multiple evaluation methods for predicting early sepsis risk in acute abdomen

This study developed and validated a nomogram for predicting sepsis risk in 383 patients with acute abdomen. Six independent predictors were identified. The model exhibited outstanding discrimination, strong calibration, and broad clinical applicability, demonstrating considerable potential for early risk stratification and clinical decision support.