Clinical predictors of COVID-19 mortality.

ABSTRACT: The coronavirus disease 2019 (COVID-19) pandemic has affected over millions of individuals and caused hundreds of thousands of deaths worldwide. It can be difficult to accurately predict mortality among COVID-19 patients presenting with a spectrum of complications, hindering the prognostication and management of the disease. We applied machine learning techniques to clinical data from a large cohort of 5,051 COVID-19 patients treated at the Mount Sinai Health System in New York City, the global COVID-19 epicenter, to predict mortality. Predictors were designed to classify patients into Deceased or Alive mortality classes and were evaluated in terms of the area under the receiver operating characteristic (ROC) curve (AUC score). Using a development cohort (n=3,841) and a systematic machine

SUBMITTER: Yadaw AS 

PROVIDER: S-EPMC7273288 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature