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

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.

Methods

We used machine learning algorithms to predict COVID-19 severity in 348 cancer patients at Memorial Sloan Kettering Cancer Center in New York City. Using only clinical variables collected on or before a patient's COVID-19 positive date (time zero), we sought to classify patients into one of three possible future outcomes: Severe-early (the patient required high levels of oxygen support within 3 days of being tested positive for COVID-19), Severe-late (the pati

SUBMITTER: Navlakha S 

PROVIDER: S-EPMC8092998 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature