[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

Journal: Zhonghua wei zhong bing ji jiu yi xue

Published Date: Apr 1, 2024

Abstract

OBJECTIVE: To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.

Authors

Zheng Xie

School of Data and Computer Science, Sun Yat-sen University, Guangzhou, People's Republic of China.
Jing Jin

College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou, China.
Dongsong Liu

Department of Emergency, Affiliated Hospital of Jiangnan University, Wuxi 214000, Jiangsu, China.
Shengyi Lu

Department of Emergency, Affiliated Hospital of Jiangnan University, Wuxi 214000, Jiangsu, China.
Hui Yu

Engineering Technology Research Center of Shanxi Province for Opto-Electric Information and Instrument, Taiyuan 030051, China. 13934603474@nuc.edu.cn.
Dong Han

Department of Radiology, Affiliated Hospital of Chengde Medical College, Chengde Hebei, 067000, P.R.China.
Wei Sun

Sutra Medical Inc, Lake Forest, CA.
Ming Huang

College of Food Science and Technology, Nanjing Agricultural University, Nanjing 210095, China.

Keywords

Algorithms Decision Trees Female Humans Intensive Care Units Machine Learning Male Middle Aged Prognosis Shock, Septic Supervised Machine Learning Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (38813626)

[Constructing a predictive model for the death risk of patients with septic shock based on supervised machine learning algorithms].

Abstract

Authors

Keywords

External Resources

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