A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers

• Published in: iScience 2021-12, Vol.24 (12), p.103523-103523, Article 103523
• Main Authors: Singh, Vivek, Kamaleswaran, Rishikesan, Chalfin, Donald, Buño-Soto, Antonio, San Roman, Janika, Rojas-Kenney, Edith, Molinaro, Ross, von Sengbusch, Sabine, Hodjat, Parsa, Comaniciu, Dorin, Kamen, Ali
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Classification Description: Virology; Diagnostics; Machine learning
• ISSN: 2589-0042; 2589-0042
• DOI: 10.1016/j.isci.2021.103523

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. [Display omitted] •Algorithm using 9 laboratory markers & age may predict severity in patients with COVID-19•Model was trained and tested on a multicenter sample of 10,937 patients•Algorithm can predict ventilator use (NPV, 0.86) and mortality (NPV, 0.94)•High NPV suggests utility as an adjunct to aid in triaging of patients with COVID-19 Classification Description: Virology; Diagnostics; Artificial intelligence; Machine learning