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

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-learnin...

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Bibliographic Details
Main Authors: Qin-Yu Zhao, Le-Ping Liu, Jing-Chao Luo, Yan-Wei Luo, Huan Wang, Yi-Jie Zhang, Rong Gui, Guo-Wei Tu, Zhe Luo
Format: Article
Language:English
Published: Frontiers Media S.A. 2021-01-01
Series:Frontiers in Medicine
Subjects:
sepsis-induced coagulopathy
dynamic prediction
machine learning
Logistic Regression
external validation
model interpretation
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2020.637434/full

Internet

https://www.frontiersin.org/articles/10.3389/fmed.2020.637434/full

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