Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study

Development and validation of machine learning models for venous thromboembolism risk assessment at admission: a retrospective study

IntroductionVenous thromboembolism (VTE) risk assessment at admission is of great importance for early screening and timely prophylaxis and management during hospitalization. The purpose of this study is to develop and validate novel risk assessment models at admission based on machine learning (ML)...

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Bibliographic Details
Main Authors: Wenbo Sheng, Xiaoli Wang, Wenxiang Xu, Zedong Hao, Handong Ma, Shaodian Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-08-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
venous thromboembolism
risk assessment model
machine learning
predictive modeling
risk stratification
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2023.1198526/full

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