Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).

Journal: Immunity, inflammation and disease

Published Date: Mar 13, 2021

Abstract

BACKGROUND: Identifying patients who may develop severe coronavirus disease 2019 (COVID-19) will facilitate personalized treatment and optimize the distribution of medical resources.

Authors

Zhixian Yao

Shanghai Medical Aid Team in Wuhan, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Xinyi Zheng

Department of Pharmacy, Huashan Hospital, Fudan University, Shanghai, China.
Zhong Zheng

National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China.
Ke Wu

Shanghai Medical Aid Team in Wuhan, Shanghai General Hospital, Shanghai Jiao Tong University, School of Medicine, Shanghai, China.
Junhua Zheng

Department of Urology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.

Keywords

Adult Aged Area Under Curve Calibration COVID-19 Decision Support Techniques Female Humans Logistic Models Machine Learning Male Middle Aged Models, Theoretical Nomograms Pandemics Prospective Studies Retrospective Studies Risk Assessment Risk Factors ROC Curve SARS-CoV-2 Sensitivity and Specificity

External Resources

View on PubMed Access via DOI PubMed (33713584)

Construction and validation of a machine learning-based nomogram: A tool to predict the risk of getting severe coronavirus disease 2019 (COVID-19).

Abstract

Authors

Keywords

External Resources

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