TY - JOUR
T1 - A deep learning approach for predicting severity of COVID-19 patients using a parsimonious set of laboratory markers
AU - Singh, Vivek
AU - Kamaleswaran, Rishikesan
AU - Chalfin, Donald
AU - Buño-Soto, Antonio
AU - San Roman, Janika
AU - Rojas-Kenney, Edith
AU - Molinaro, Ross
AU - von Sengbusch, Sabine
AU - Hodjat, Parsa
AU - Comaniciu, Dorin
AU - Kamen, Ali
N1 - Funding Information:
This research and development project was supported by Siemens Healthineers , USA.
Publisher Copyright:
© 2021 The Authors
PY - 2021/12/17
Y1 - 2021/12/17
N2 - 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.
AB - 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.
KW - Artificial intelligence
KW - Classification Description: Virology
KW - Diagnostics
KW - Machine learning
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U2 - 10.1016/j.isci.2021.103523
DO - 10.1016/j.isci.2021.103523
M3 - Article
AN - SCOPUS:85121150748
SN - 2589-0042
VL - 24
JO - iScience
JF - iScience
IS - 12
M1 - 103523
ER -