Results: A total of 499 patients were used to develop the models in the training data set. An
independent data set of 201 patients was used to test the models. The model developed by …

Abstract Background and Aim Severe acute pancreatitis (SAP) in patients progresses rapidly
and can cause multiple organ failures associated with high mortality. We aimed to train a …

Background and Aims This study aimed to develop an interpretable random forest model for
predicting severe acute pancreatitis (SAP). Methods Clinical and laboratory data of 648 …

Objective Aim to establish a multimodal model for predicting severe acute pancreatitis (SAP)
using machine learning (ML) and deep learning (DL). Methods In this multicentre …

Objective To develop a model for the early prediction of severe acute pancreatitis based on
the revised Atlanta classification of acute pancreatitis. Methods Clinical data of 1308 patients …

Background The available prognostic scoring systems for acute pancreatitis have limitations
that restrict their clinical value. Aims To develop a decision model based on classification …

Background Acute pancreatitis (AP) is the most common pancreatic disease. Predicting the
severity of AP is critical for making preventive decisions. However, the performance of …

Background Machine learning (ML) algorithms are widely applied in building models of
medicine due to their powerful studying and generalizing ability. This study aims to explore …

Background Severe acute pancreatitis (SAP) can progress to lung and kidney dysfunction,
and blood clotting within 48 hours of its onset, and is associated with a high mortality rate …

Background Acute pancreatitis (AP) is one of the most common causes of gastrointestinal-
related hospitalizations in the United States. Severe AP (SAP) is associated with a mortality …