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A Simple and Quick Screening Method for Intrapulmonary Vascular Dilation in Cirrhotic Patients Based on Machine Learning
Li, Yu-Jie1; Tang, Xi2,3,4; Li, Peng1; Yang, Zhi-Yong1; Zhi, Hong-Yu1; Li, Xiao-Jun1; Chen, Yang1; Deng, Peng1; Qin, Xiao-Lin2,3; Gu, Jian-Teng1
2021-04-29
摘要Background and Aims: Screening for hepatopulmonary syndrome in cirrhotic patients is limited due to the need to perform contrast enhanced echocardiography (CEE) and arterial blood gas (ABG) analysis. We aimed to develop a simple and quick method to screen for the presence of in-trapulmonary vascular dilation (IPVD) using noninvasive and easily available variables with machine learning (ML) algorithms. Methods: Cirrhotic patients were enrolled from our hospital. All eligible patients underwent CEE, ABG analysis and physical examination. We developed a two-step model based on three ML algorithms, namely, adap-tive boosting (termed AdaBoost), gradient boosting deci-sion tree (termed GBDT) and eXtreme gradient boosting (termed Xgboost). Noninvasive variables were input in the first step (the NI model), and for the second step (the NIBG model), a combination of noninvasive variables and ABG re-sults were used. Model performance was determined by the area under the curve of receiver operating characteristics (AUCROCs), precision, recall, F1-score and accuracy. Re-sults: A total of 193 cirrhotic patients were ultimately ana-lyzed. The AUCROCs of the NI and NIBG models were 0.850
关键词Hepatopulmonary syndrome Intrapulmonary vascular dilation Cirrhosis Screening Machine learning
DOI10.14218/JCTH.2020.00184
发表期刊JOURNAL OF CLINICAL AND TRANSLATIONAL HEPATOLOGY
ISSN2225-0719
页码8
通讯作者Chen, Yu-Wen(chenyuwen@cigit.ac.cn) ; Yi, Bin(yibin1974@163.com)
收录类别SCI
WOS记录号WOS:000702908100001
语种英语