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Applications of machine learning models in the prediction of gastric cancer risk in patients after Helicobacter pylori eradication

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Applications of machine learning models in the prediction of gastric cancer risk in patients after Helicobacter pylori eradication

Alimentary Pharmacology & Therapeutics

Fecha de publicación: 24 de enero de 2021

DOI: https://doi.org/10.1111/apt.16272

Autores: Wai K. Leung, Ka Shing Cheung, Bofei Li, Simon Y. K. Law, Thomas K. L. Lui

Background: The risk of gastric cancer after Helicobacter pylori (H. pylori) eradication remains unknown. Aim: To evaluate the performances of seven different machine learning models in predicting gastric cancer risk after H. pylori eradication. Methods: We identified H. pylori‐infected patients who had received clarithromycin‐based triple therapy between 2003 and 2014 in Hong Kong. Patients were divided into training (n = 64 238) and validation sets (n = 25 330), according to period of eradication therapy. The data were used to construct seven machine learning models to predict risk of gastric cancer development within 5 years after H. pylori eradication. A total of 26 clinical variables were input into these models. The performances were measured by the area under receiver operating characteristic curve (AUC) analysis.

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