A Deep Learning Approach for Predicting Severity of COVID-19 Patients Using a Parsimonious Set of Laboratory Markers

Overview

Journal iScience

Publisher Cell Press

Date 2021 Dec 6

PMID 34870131

Citations 11

Authors

Vivek Singh

Rishikesan Kamaleswaran

Donald Chalfin

Antonio Buno-Soto

Janika San Roman

Edith Rojas-Kenney

Ross Molinaro

Sabine von Sengbusch

Parsa Hodjat

Dorin Comaniciu

Ali Kamen

Affiliations

Soon will be listed here.

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.

Citing Articles

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Concordance and generalization of an AI algorithm with real-world clinical data in the pre-omicron and omicron era.

Yilmaz G, Sezer S, Bastug A, Singh V, Gopalan R, Aydos O Heliyon. 2024; 10(3):e25410.

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The Diagnostic, Prognostic, and Therapeutic Potential of Cell-Free DNA with a Special Focus on COVID-19 and Other Viral Infections.

Hovhannisyan G, Harutyunyan T, Aroutiounian R, Liehr T Int J Mol Sci. 2023; 24(18).

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Diagnosis of COVID-19 Using Chest X-ray Images and Disease Symptoms Based on Stacking Ensemble Deep Learning.

AlMohimeed A, Saleh H, El-Rashidy N, Saad R, El-Sappagh S, Mostafa S Diagnostics (Basel). 2023; 13(11).

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The COVID-19 pandemic in various restriction policy scenarios based on the dynamic social contact rate.

Hu H, Xiong S, Zhang X, Liu S, Gu L, Zhu Y Heliyon. 2023; 9(3):e14533.

PMID: 36945346 PMC: 10017169. DOI: 10.1016/j.heliyon.2023.e14533.

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