Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy

Mechanical Learning for Prediction of Sepsis-Associated Encephalopathy

Objective: The study aims to develop a mechanical learning model as a predictive model for predicting the appearance of sepsis-associated encephalopathy (SAE).Materials and Methods: The prediction model was developed in a primary cohort of 2,028 sepsis patients from June 2001 to October 2012, retrie...

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Bibliographic Details
Main Authors: Lina Zhao, Yunying Wang, Zengzheng Ge, Huadong Zhu, Yi Li
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-11-01
Series:Frontiers in Computational Neuroscience
Subjects:
sepsis
sepsis-associated encephalopathy
encephalopathy
delirium
nomogram
mechanical learning
Online Access:https://www.frontiersin.org/articles/10.3389/fncom.2021.739265/full

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https://www.frontiersin.org/articles/10.3389/fncom.2021.739265/full

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