Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

Early Prediction of Mortality, Severity, and Length of Stay in the Intensive Care Unit of Sepsis Patients Based on Sepsis 3.0 by Machine Learning Models

Background: Early prediction of the clinical outcome of patients with sepsis is of great significance and can guide treatment and reduce the mortality of patients. However, it is clinically difficult for clinicians.Methods: A total of 2,224 patients with sepsis were involved over a 3-year period (20...

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Bibliographic Details
Main Authors: Longxiang Su, Zheng Xu, Fengxiang Chang, Yingying Ma, Shengjun Liu, Huizhen Jiang, Hao Wang, Dongkai Li, Huan Chen, Xiang Zhou, Na Hong, Weiguo Zhu, Yun Long
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-06-01
Series:Frontiers in Medicine
Subjects:
sepsis
prediction
machine learning
outcome
sequential (sepsis-related) organ failure assessment
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2021.664966/full

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https://www.frontiersin.org/articles/10.3389/fmed.2021.664966/full

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