An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

An Interpretable Early Dynamic Sequential Predictor for Sepsis-Induced Coagulopathy Progression in the Real-World Using Machine Learning

Sepsis-associated coagulation dysfunction greatly increases the mortality of sepsis. Irregular clinical time-series data remains a major challenge for AI medical applications. To early detect and manage sepsis-induced coagulopathy (SIC) and sepsis-associated disseminated intravascular coagulation (D...

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Autores principales: Ruixia Cui, Wenbo Hua, Kai Qu, Heran Yang, Yingmu Tong, Qinglin Li, Hai Wang, Yanfen Ma, Sinan Liu, Ting Lin, Jingyao Zhang, Jian Sun, Chang Liu
Formato: article
Lenguaje:EN
Publicado: Frontiers Media S.A. 2021
Materias:
SIC
sepsis-associated DIC
irregular time-series data
early real-time prediction
machine learning
Medicine (General)
R5-920
Acceso en línea:https://doaj.org/article/2d7495b4717845858bc66a17cab36f98
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https://doaj.org/article/2d7495b4717845858bc66a17cab36f98

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