Machine learning for early prediction of sepsis-associated acute brain injury

BACKGROUND: Sepsis-associated encephalopathy (SAE) is defined as diffuse brain dysfunction associated with sepsis and leads to a high mortality rate. We aimed to develop and validate an optimal machine-learning model based on clinical features for early predicting sepsis-associated acute brain injur...

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Detalles Bibliográficos
Autores principales: Ge, Chenglong, Deng, Fuxing, Chen, Wei, Ye, Zhiwen, Zhang, Lina, Ai, Yuhang, Zou, Yu, Peng, Qianyi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575145/
https://www.ncbi.nlm.nih.gov/pubmed/36262275
http://dx.doi.org/10.3389/fmed.2022.962027

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575145/
https://www.ncbi.nlm.nih.gov/pubmed/36262275
http://dx.doi.org/10.3389/fmed.2022.962027

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