Evaluating machine learning models for sepsis prediction: A systematic review of methodologies

Author:

Deng Hong-Fei,Sun Ming-Wei,Wang Yu,Zeng Jun,Yuan Ting,Li Ting,Li Di-Huan,Chen Wei,Zhou Ping,Wang Qi,Jiang Hua

Funder

National Natural Science Foundation of China

Department of Science and Technology of Sichuan Province

NSAF Joint Fund

Publisher

Elsevier BV

Subject

Multidisciplinary

Reference46 articles.

1. Surviving sepsis campaign: guidelines on the management of critically Ill adults with coronavirus disease 2019 (COVID-19);Alhazzani;Crit. Care Med.,2020

2. Evaluation of a machine learning algorithm for up to 48-hour advance prediction of sepsis using six vital signs;Barton;Comput. Biol. Med.,2019

3. Characterizing and managing missing structured data in electronic health records: data analysis;Beaulieu-Jones;JMIR Med. Inform.,2018

4. Machine learning for early detection of sepsis: an internal and temporal validation study;Bedoya;JAMIAOpen.,2020

5. Machine learning models for analysis of vital signs dynamics: a case for sepsis onset prediction;Bloch;J. Healthc. Eng.,2019

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