Early prediction system for acute severe pancreatitis based on machine learning / 中华急诊医学杂志

ABSTRACT

Acute pancreatitis (AP) is a common disease faced by clinicians. Severe acute pancreatitis (SAP) has a high mortality rate, so early identification of patients who may develop into SAP is of great significance for guiding treatment. Machine learning is a multi-layer representational learning algorithm that analyzes and obtains laws from existing data and uses these laws to make predictions on unknown data. This study established an SAP prediction scoring system based on machine learning, which can predict the SAP risk of patients within 24 hours. The prediction accuracy rate is as high as 87.36% and AUC 94.11%. The model can better assist clinical decision-making and treatment, and guide doctors to make relevant interventions earlier.