Severity Prediction for COVID-19 Patients via Recurrent Neural Networks

Lee, Junghwan; Ta, Casey; Kim, Jae Hyun; Liu, Cong; Weng, Chunhua

The novel coronavirus disease-2019 (COVID-19) pandemic has threatened the health of tens of millions of people worldwide and imposed heavy burden on global healthcare systems. 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) prior to hospital admission using recurrent neural networks. The model predicts risk score that represents the probability for a patient to progress into severe status (mechanical ventilation, tracheostomy, or death) after being infected with COVID-19. The model achieved 0.846 area under the receiver operating characteristic curve in predicting patients’ outcomes averaged over 5-fold cross validation. While many of the existing models use features obtained after diagnosis of COVID-19, our proposed model only utilizes a patient’s historical EHR to enable proactive risk management at the time of hospital admission.