Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model

Predicting venous thromboembolism in hospitalized trauma patients: a combination of the Caprini score and data-driven machine learning model

Abstract Background Venous thromboembolism (VTE) is a common complication of hospitalized trauma patients and has an adverse impact on patient outcomes. However, there is still a lack of appropriate tools for effectively predicting VTE for trauma patients. We try to verify the accuracy of the Caprin...

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Bibliographic Details
Main Authors: Lingxiao He, Lei Luo, Xiaoling Hou, Dengbin Liao, Ran Liu, Chaowei Ouyang, Guanglin Wang
Format: Article
Language:English
Published: BMC 2021-05-01
Series:BMC Emergency Medicine
Subjects:
Venous thromboembolism
Wounds and injuries
Risk assessment
Machine learning
Models, statistical
Online Access:https://doi.org/10.1186/s12873-021-00447-x

Internet

https://doi.org/10.1186/s12873-021-00447-x

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