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

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

BackgroundSepsis-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 injury....

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Bibliographic Details
Main Authors: Chenglong Ge, Fuxing Deng, Wei Chen, Zhiwen Ye, Lina Zhang, Yuhang Ai, Yu Zou, Qianyi Peng
Format: Article
Language:English
Published: Frontiers Media S.A. 2022-10-01
Series:Frontiers in Medicine
Subjects:
sepsis-associated encephalopathy
machine learning
prediction
MIMIC III
light gradient boosting machine
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2022.962027/full

Internet

https://www.frontiersin.org/articles/10.3389/fmed.2022.962027/full

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