A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
Vivek Singh,
Rishikesan Kamaleswaran,
Donald Chalfin,
Antonio Buño-Soto,
Janika San Roman,
Edith Rojas-Kenney,
Ross Molinaro,
Sabine von Sengbusch,
Parsa Hodjat,
Dorin Comaniciu,
Ali Kamen
Affiliations
Vivek Singh
Siemens Healthineers, Digital Technology and Innovation, 755 College Road East, Princeton, NJ 08540, USA
Rishikesan Kamaleswaran
Emory University School of Medicine WMB, 1010 Woodruff Circle, Suite 4127, Atlanta, GA 30322, USA
Donald Chalfin
Siemens Healthineers, Laboratory Diagnostics, 511 Benedict Avenue, Tarrytown, NY 10591, USA; Jefferson College of Population Health of Thomas Jefferson University, 901 Walnut Street, Philadelphia, PA 19107, USA
Antonio Buño-Soto
Department of Laboratory Medicine, Hospital Universitario La Paz, Madrid, Spain
Janika San Roman
Siemens Healthineers, Laboratory Diagnostics, 511 Benedict Avenue, Tarrytown, NY 10591, USA
Edith Rojas-Kenney
Siemens Healthineers, Laboratory Diagnostics, 511 Benedict Avenue, Tarrytown, NY 10591, USA
Ross Molinaro
Siemens Healthineers, Laboratory Diagnostics, 511 Benedict Avenue, Tarrytown, NY 10591, USA
Sabine von Sengbusch
Siemens Healthineers, Laboratory Diagnostics, 511 Benedict Avenue, Tarrytown, NY 10591, USA
Parsa Hodjat
Department of Pathology and Genomic Medicine, Houston Methodist Hospital, 6565 Fannin Street, Houston, TX 77030, USA
Dorin Comaniciu
Siemens Healthineers, Digital Technology and Innovation, 755 College Road East, Princeton, NJ 08540, USA
Ali Kamen
Siemens Healthineers, Digital Technology and Innovation, 755 College Road East, Princeton, NJ 08540, USA; Corresponding author
Summary: 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.