Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models

Prediction of in-hospital adverse clinical outcomes in patients with pulmonary thromboembolism, machine learning based models

BackgroundPulmonary thromboembolism (PE) is the third leading cause of cardiovascular events. The conventional modeling methods and severity risk scores lack multiple laboratories, paraclinical and imaging data. Data science and machine learning (ML) based prediction models may help better predict o...

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Bibliographic Details
Main Authors: Yaser Jenab, Kaveh Hosseini, Zahra Esmaeili, Saeed Tofighi, Hamid Ariannejad, Houman Sotoudeh
Format: Article
Language:English
Published: Frontiers Media S.A. 2023-03-01
Series:Frontiers in Cardiovascular Medicine
Subjects:
machine learing
outcome analysis
risk factors
logistic models
gradient boosting machine
pulmonary embolism
Online Access:https://www.frontiersin.org/articles/10.3389/fcvm.2023.1087702/full

Internet

https://www.frontiersin.org/articles/10.3389/fcvm.2023.1087702/full

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