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

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 over conven...

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Bibliographic Details
Published in:	Journal of translational medicine Vol. 18; no. 1; pp. 462 - 14
Main Authors:	Hou, Nianzong, Li, Mingzhe, He, Lu, Xie, Bing, Wang, Lin, Zhang, Rumin, Yu, Yong, Sun, Xiaodong, Pan, Zhengsheng, Wang, Kai
Format:	Journal Article
Language:	English
Published:	London BioMed Central 07.12.2020 BioMed Central Ltd Springer Nature B.V BMC
Subjects:	Algorithms Biomedical and Life Sciences Biomedicine Blood pressure Care and treatment Critical care Critical Care and Anesthesia Critically ill Health aspects Hospital Mortality Hospitals Humans Infections Intensive care Laboratories Learning algorithms Logistic Models Logistic regression Machine Learning Medical prognosis Medicine/Public Health MIMIC-III Mortality Nomograms Pain Patient outcomes Patients Physiology Prediction models Prognosis Public health Regression analysis ROC Curve SAPS-II score Sepsis Sepsis - diagnosis Sepsis-3 Survival Variables Vital signs Xgboost China United States > US Massachusetts Logistic regression Xgboost MIMIC-III Sepsis-3 SAPS-II score Machine learning
ISSN:	1479-5876, 1479-5876
Online Access:	Get full text
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