Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

Journal: Critical care medicine

Published Date: Dec 1, 2020

Abstract

OBJECTIVES: Sepsis is caused by infection and subsequent overreaction of immune system and will severely threaten human life. The early prediction is important for the treatment of sepsis. This report aims to develop an early prediction method for sepsis 6 hours ahead on the basis of clinical electronic health records.

Authors

Zhengling He
Lidong Du

Institute of Electronics, Chinese Academy of Sciences, Beijing, China; Personalized Management of Chronic Respiratory Disease, Chinese Academy of Medical Sciences, China.
Pengfei Zhang

Key Laboratory of Cardiovascular Remodeling and Function Research, Chinese Ministry of Education and Chinese National Health Commission, Department of Cardiology, Qilu Hospital of Shandong University. N0.107 Wenhuaxi Road, Jinan, Shanodng Province, China. Electronic address: pengf-zhang@163.com.
Rongjian Zhao
Xianxiang Chen
Zhen Fang

Keywords

Artificial Intelligence Deep Learning Early Diagnosis Electronic Health Records Humans Intensive Care Units Neural Networks, Computer Reproducibility of Results Sepsis Supervised Machine Learning

External Resources

View on PubMed Access via DOI PubMed (33044286)

Early Sepsis Prediction Using Ensemble Learning With Deep Features and Artificial Features Extracted From Clinical Electronic Health Records.

Abstract

Authors

Keywords

External Resources

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