  The COVID-19 pandemic has created an urgent need for robust, scalable
monitoring tools supporting stratification of high-risk patients. This research
aims to develop and validate prediction models, using the UK Biobank, to
estimate COVID-19 mortality risk in confirmed cases. From the 11,245
participants testing positive for COVID-19, we develop a data-driven random
forest classification model with excellent performance (AUC: 0.91), using
baseline characteristics, pre-existing conditions, symptoms, and vital signs,
such that the score could dynamically assess mortality risk with disease
deterioration. We also identify several significant novel predictors of
COVID-19 mortality with equivalent or greater predictive value than established
high-risk comorbidities, such as detailed anthropometrics and prior acute
kidney failure, urinary tract infection, and pneumonias. The model design and
feature selection enables utility in outpatient settings. Possible applications
include supporting individual-level risk profiling and monitoring disease
progression across patients with COVID-19 at-scale, especially in
hospital-at-home settings.
