Objective To evaluate the success of artificial intelligence for early prediction of severe
course, survival, and intensive care unit (ICU) requirement in patients with acute pancreatitis …

Background Acute pancreatitis (AP) with critical illness is linked to increased morbidity and
mortality. Current risk scores to identify high-risk AP patients have certain limitations …

Background Predicting the severity of acute pancreatitis (AP) early poses a challenge in
clinical practice. While there are well-established clinical scoring tools, their actual predictive …

Background. Early and accurate evaluation of severity and prognosis in acute pancreatitis
(AP), especially at the time of admission is very significant. This study was aimed to develop …

Background Acute pancreatitis (AP) is a potentially severe or even fatal inflammation of the
pancreas. Early identification of patients at high risk for developing a severe course of the …

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

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 …

Objective Artificial intelligence (AI) prediction is increasingly used for decision making in
health care, but its application for adverse outcomes in emergency department (ED) patients …

The clinical course of acute pancreatitis (AP) can be variable depending on the severity of
the disease, and it is crucial to predict the probability of organ failure to initiate early …

Background: Current approaches to predicting intervention needs and mortality have
reached 65–85% accuracy, which falls below clinical decision-making requirements in …