Noninvasive prediction of esophagogastric varices in hepatitis B: An extreme gradient boosting model based on ultrasound and serology.
Journal:
World journal of gastroenterology
PMID:
40248058
Abstract
BACKGROUND: Severe esophagogastric varices (EGVs) significantly affect prognosis of patients with hepatitis B because of the risk of life-threatening hemorrhage. Endoscopy is the gold standard for EGV detection but it is invasive, costly and carries risks. Noninvasive predictive models using ultrasound and serological markers are essential for identifying high-risk patients and optimizing endoscopy utilization. Machine learning (ML) offers a powerful approach to analyze complex clinical data and improve predictive accuracy. This study hypothesized that ML models, utilizing noninvasive ultrasound and serological markers, can accurately predict the risk of EGVs in hepatitis B patients, thereby improving clinical decision-making.