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首页> 外文期刊>Clinical gastroenterology and hepatology: the official clinical practice journal of the American Gastroenterological Association >Machine Learning-based Development and Validation of a Scoring System for Screening High-Risk Esophageal Varices
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Machine Learning-based Development and Validation of a Scoring System for Screening High-Risk Esophageal Varices

机译:基于机器学习的开发和验证评分系统,用于筛选高风险食管静脉曲张

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摘要

BACKGROUND & AIMS: Many patients with cirrhosis who undergo esophagogastroduodenoscopy (EGD) screening for esophageal varices (EVs) are found to have no or only small EVs. Endoscopic screening for EVs is therefore a potentially deferrable procedure that increases patient risk and healthcare cost. We developed and validated a scoring system, based on readily-available data, to reliably identify patients with EVs that need treatment.
机译:背景和目的:许多肝硬化患者接受食管毒素(EGD)筛选食管静脉曲张(EVS)的筛选有没有或仅小型电源。 因此,EVS的内窥镜筛查是一种潜在的推迟程序,可以增加患者风险和医疗费用。 我们开发并验证了基于易用数据的评分系统,可靠地识别需要治疗的EVS患者。

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