Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

Journal: BMC medical informatics and decision making

Published Date: Oct 2, 2020

Abstract

BACKGROUND: Early and accurate identification of sepsis patients with high risk of in-hospital death can help physicians in intensive care units (ICUs) make optimal clinical decisions. This study aimed to develop machine learning-based tools to predict the risk of hospital death of patients with sepsis in ICUs.

Authors

Guilan Kong

National Institute of Health Data Science, Peking University, Beijing, China. guilan.kong@hsc.pku.edu.cn.
Ke Lin

Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA 90095, USA.
Yonghua Hu

Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.

Keywords

Adult Aged Aged, 80 and over Critical Care Critical Illness Female Hospital Mortality Humans Intensive Care Units Machine Learning Male Middle Aged Models, Theoretical Sepsis

External Resources

View on PubMed Access via DOI PubMed (33008381)

Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU.

Abstract

Authors

Keywords

External Resources

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