Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation

Interpretable machine learning model for early prediction of 28-day mortality in ICU patients with sepsis-induced coagulopathy: development and validation

Abstract Objective Sepsis-induced coagulopathy (SIC) is extremely common in individuals with sepsis, significantly associated with poor outcomes. This study attempted to develop an interpretable and generalizable machine learning (ML) model for early predicting the risk of 28-day death in patients w...

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Bibliographic Details
Main Authors: Shu Zhou, Zongqing Lu, Yu Liu, Minjie Wang, Wuming Zhou, Xuanxuan Cui, Jin Zhang, Wenyan Xiao, Tianfeng Hua, Huaqing Zhu, Min Yang
Format: Article
Language:English
Published: BMC 2024-01-01
Series:European Journal of Medical Research
Subjects:
Sepsis induced coagulopathy
Gradient boosting decision tree
Machine learning
Shapley additive explanations
Local interpretable model-agnostic explanations
Online Access:https://doi.org/10.1186/s40001-023-01593-7

Internet

https://doi.org/10.1186/s40001-023-01593-7

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