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

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 regression (LR)...

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Detalles Bibliográficos
Autores principales: Wang, Geng, Xu, Jiatang, Lin, Xixia, Lai, Weijie, Lv, Lin, Peng, Senyi, Li, Kechen, Luo, Mingli, Chen, Jiale, Zhu, Dongxi, Chen, Xiong, Yao, Chen, Wu, Shaoxu, Huang, Kai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2023
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399014/
https://www.ncbi.nlm.nih.gov/pubmed/37533004
http://dx.doi.org/10.1186/s12872-023-03363-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399014/
https://www.ncbi.nlm.nih.gov/pubmed/37533004
http://dx.doi.org/10.1186/s12872-023-03363-z

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