An increasing number of COVID-19 cases worldwide has overwhelmed the
healthcare system. Physicians are struggling to allocate resources and
to focus their attention on high-risk patients, partly because early
identification of high-risk individuals is difficult. This can be
attributed to the fact that COVID-19 is a novel disease and its
pathogenesis is still partially understood. However, machine learning
algorithms have the capability to correlate a large number of parameters
within a short period of time to identify the predictors of disease
outcome. Implementing such an algorithm to predict high-risk individuals
during the early stages of infection, would be helpful in decision
making for clinicians. Here, we propose recommendations to integrate
machine learning model with electronic health records so that a
real-time risk score can be developed for COVID-19.