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.