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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Principais autores:	Shu Zhou, Zongqing Lu, Yu Liu, Minjie Wang, Wuming Zhou, Xuanxuan Cui, Jin Zhang, Wenyan Xiao, Tianfeng Hua, Huaqing Zhu, Min Yang
פורמט:	Artigo
שפה:	Inglês
יצא לאור:	BMC 2024-01-01
סדרה:	European Journal of Medical Research
נושאים:	Sepsis induced coagulopathy Gradient boosting decision tree Machine learning Shapley additive explanations Local interpretable model-agnostic explanations
גישה מקוונת:	https://doi.org/10.1186/s40001-023-01593-7
תגים:	הוספת תג אין תגיות, היה/י הראשונ/ה לתייג את הרשומה!