A Machine-Learning Approach for Dynamic Prediction of Sepsis-Induced Coagulopathy in Critically Ill Patients With Sepsis

Background: Sepsis-induced coagulopathy (SIC) denotes an increased mortality rate and poorer prognosis in septic patients. Objectives: Our study aimed to develop and validate machine-learning models to dynamically predict the risk of SIC in critically ill patients with sepsis. Methods: Machine-learn...

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Detalles Bibliográficos
Autores principales: Zhao, Qin-Yu, Liu, Le-Ping, Luo, Jing-Chao, Luo, Yan-Wei, Wang, Huan, Zhang, Yi-Jie, Gui, Rong, Tu, Guo-Wei, Luo, Zhe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859637/
https://www.ncbi.nlm.nih.gov/pubmed/33553224
http://dx.doi.org/10.3389/fmed.2020.637434

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7859637/
https://www.ncbi.nlm.nih.gov/pubmed/33553224
http://dx.doi.org/10.3389/fmed.2020.637434

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