A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
Vivek Kumar Singh, Rishikesan Kamaleswaran, Donald B. Chalfin, Antonio Buño Soto, Janika San Roman, Edith Rojas-Kenney, Ross J. Molinaro, Sabine von Sengbusch, Parsa Hodjat, Dorin Comaniciu, Ali Kamen
Abstract
The SARS-CoV-2 virus has caused tremendous healthcare burden worldwide. Our focus was to develop a practical and easy-to-deploy system to predict the severe manifestation of disease in patients with COVID-19 with an aim to assist clinicians in triage and treatment decisions. Our proposed predictive algorithm is a trained artificial intelligence-based network using 8,427 COVID-19 patient records from four healthcare systems. The model provides a severity risk score along with likelihoods of various clinical outcomes, namely ventilator use and mortality. The trained model using patient age and nine laboratory markers has the prediction accuracy with an area under the curve (AUC) of 0.78, 95% CI: 0.77-0.82, and the negative predictive value NPV of 0.86, 95% CI: 0.84-0.88 for the need to use a ventilator and has an accuracy with AUC of 0.85, 95% CI: 0.84-0.86, and the NPV of 0.94, 95% CI: 0.92-0.96 for predicting in-hospital 30-day mortality.