Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and posed enormous burden on the global healthcare systems. Many prediction models have been proposed to fight against the pandemic. In this paper, we propose a model to predict whether a patient infected with COVID-19 will develop severe outcomes based only on the patient’s historical electronic health records (EHR) using recurrent neural networks (RNN). The predicted severity risk score represents the probability for a person to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient’s historical EHR so that it can enable proactive risk management before or at the time of hospital admission.