Use of AI to assess COVID-19 variant impacts on hospitalization, ICU, and death.
Autor: | Hilal W; Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada., Chislett MG; School of Engineering, University of Guelph, Guelph, ON, Canada., Snider B; College of Engineering, Mathematics, and Physical Sciences, University of Exeter, Exeter, United Kingdom., McBean EA; School of Engineering, University of Guelph, Guelph, ON, Canada., Yawney J; Adastra Corporation, Toronto, ON, Canada., Gadsden SA; Department of Mechanical Engineering, McMaster University, Hamilton, ON, Canada. |
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Jazyk: | angličtina |
Zdroj: | Frontiers in artificial intelligence [Front Artif Intell] 2022 Nov 30; Vol. 5, pp. 927203. Date of Electronic Publication: 2022 Nov 30 (Print Publication: 2022). |
DOI: | 10.3389/frai.2022.927203 |
Abstrakt: | The rapid spread of COVID-19 and its variants have devastated communities worldwide, and as the highly transmissible Omicron variant becomes the dominant strain of the virus in late 2021, the need to characterize and understand the difference between the new variant and its predecessors has been an increasing priority for public health authorities. Artificial Intelligence has played a significant role in the analysis of various facets of COVID-19 since the early stages of the pandemic. This study proposes the use of AI, specifically an XGBoost model, to quantify the impact of various medical risk factors (or "population features") on the possibility of a patient outcome resulting in hospitalization, ICU admission, or death. The results are compared between the Delta and Omicron COVID-19 variants. Results indicated that older age and an unvaccinated patient status most consistently correspond as the most significant population features contributing to all three scenarios (hospitalization, ICU, death). The top 15 features for each variant-outcome scenario were determined, which most frequently included diabetes, cardiovascular disease, chronic kidney disease, and complications of pneumonia as highly significant population features contributing to serious illness outcomes. The Delta/Hospitalization model returned the highest performance metric scores for the area under the receiver operating characteristic (AUROC), F1, and Recall, while Omicron/ICU and Omicron/Hospitalization had the highest accuracy and precision values, respectively. The recall was found to be above 0.60 in most cases (with only two exceptions), indicating that the total number of false positives was generally minimized (accounting for more of the people who would theoretically require medical care). Competing Interests: JY was employed by Adastra Corporation. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2022 Hilal, Chislett, Snider, McBean, Yawney and Gadsden.) |
Databáze: | MEDLINE |
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