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 analyse 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 such that irreversible damage could be prevented. Here, we propose recommendations to develop prognostic machine learning models using electronic health records so that a real-time risk score can be developed for COVID-19.