Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

Predicting 30-days mortality for MIMIC-III patients with sepsis-3: a machine learning approach using XGboost

Abstract Background Sepsis is a significant cause of mortality in-hospital, especially in ICU patients. Early prediction of sepsis is essential, as prompt and appropriate treatment can improve survival outcomes. Machine learning methods are flexible prediction algorithms with potential advantages ov...

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Bibliographic Details
Main Authors: Nianzong Hou, Mingzhe Li, Lu He, Bing Xie, Lin Wang, Rumin Zhang, Yong Yu, Xiaodong Sun, Zhengsheng Pan, Kai Wang
Format: Article
Language:English
Published: BMC 2020-12-01
Series:Journal of Translational Medicine
Subjects:
MIMIC-III
Sepsis-3
Machine learning
Xgboost
Logistic regression
SAPS-II score
Online Access:https://doi.org/10.1186/s12967-020-02620-5

Internet

https://doi.org/10.1186/s12967-020-02620-5

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