Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers

Predicting sepsis in-hospital mortality with machine learning: a multi-center study using clinical and inflammatory biomarkers

Abstract Background This study aimed to develop and validate an interpretable machine-learning model that utilizes clinical features and inflammatory biomarkers to predict the risk of in-hospital mortality in critically ill patients suffering from sepsis. Methods We enrolled all patients diagnosed w...

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Bibliographic Details
Main Authors: Guyu Zhang, Fei Shao, Wei Yuan, Junyuan Wu, Xuan Qi, Jie Gao, Rui Shao, Ziren Tang, Tao Wang
Format: Article
Language:English
Published: BMC 2024-03-01
Series:European Journal of Medical Research
Subjects:
Sepsis
Prediction
Machining learning
Intensive care unit
XGBoost
Online Access:https://doi.org/10.1186/s40001-024-01756-0

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