Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism

Machine learning-based models for predicting mortality and acute kidney injury in critical pulmonary embolism

Abstract Objectives We aimed to use machine learning (ML) algorithms to risk stratify the prognosis of critical pulmonary embolism (PE). Material and methods In total, 1229 patients were obtained from MIMIC-IV database. Main outcomes were set as all-cause mortality within 30 days. Logistic regressio...

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Bibliographic Details
Main Authors: Geng Wang, Jiatang Xu, Xixia Lin, Weijie Lai, Lin Lv, Senyi Peng, Kechen Li, Mingli Luo, Jiale Chen, Dongxi Zhu, Xiong Chen, Chen Yao, Shaoxu Wu, Kai Huang
Format: Article
Language:English
Published: BMC 2023-08-01
Series:BMC Cardiovascular Disorders
Subjects:
Pulmonary embolism (PE)
Machine learning (ML)
Prognosis
Mortality
Intensive care unit
Online Access:https://doi.org/10.1186/s12872-023-03363-z

Internet

https://doi.org/10.1186/s12872-023-03363-z

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