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

• 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: England BioMed Central Ltd 07.12.2020; BioMed Central; BMC
• Subjects: Algorithms; Blood pressure; Care and treatment; Critical care; Critically ill; Health aspects; Hospital Mortality; Hospitals; Humans; Infections; Intensive care; Laboratories; Learning algorithms; Logistic Models; Logistic regression; Machine Learning; Medical prognosis; MIMIC-III; Mortality; Nomograms; 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; SAPS-II score; Sepsis-3; Machine learning

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 conventional regr...