Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

Journal: BMC infectious diseases

PMID: 33941093

Abstract

BACKGROUND: Accurately predicting outcomes for cancer patients with COVID-19 has been clinically challenging. Numerous clinical variables have been retrospectively associated with disease severity, but the predictive value of these variables, and how multiple variables interact to increase risk, remains unclear.

Authors

Saket Navlakha

Integrative Biology Laboratory, The Salk Institute for Biological Studies, La Jolla, CA, USA. navlakha@salk.edu.
Sejal Morjaria

Infectious Disease, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Rocio Perez-Johnston

Memorial Sloan Kettering Cancer Center, 1275 York Ave, New York, NY, 10065, USA.
Allen Zhang

MD/PhD Program, Faculty of Medicine, University of British Columbia, Vancouver, BC, Canada.
Ying Taur

Infectious Disease, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA. taury@mskcc.org.

Keywords

Aged Aged, 80 and over Algorithms Area Under Curve Comorbidity COVID-19 Decision Support Systems, Clinical Female Humans Machine Learning Male Middle Aged Neoplasms New York City Prognosis Respiration, Artificial Retrospective Studies Risk Factors ROC Curve Severity of Illness Index

External Resources

View on PubMed Access via DOI PubMed (33941093)

Projecting COVID-19 disease severity in cancer patients using purposefully-designed machine learning.

Abstract

Authors

Keywords

External Resources

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