ArticleHeliyon2023
Severity-onset prediction of COVID-19 via artificial-intelligence analysis of multivariate factors.
Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed, 4 citations in OpenAlex.
- Serum asprosin levels as a potential prognostic biomarker in COVID-19: insights for future respiratory viral pandemics.BMC infectious diseases · 2025Article
- Prediction models based on machine learning algorithms for COVID-19 severity risk.BMC public health · 2025Article
- Association between Vaccination Status for COVID-19 and the Risk of Severe Symptoms during the Endemic Phase of the Disease.Vaccines · 2023Article
Corrections and comments
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Authors and funding
19 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Progression to a severe condition remains a major risk factor for the COVID-19 mortality. Robust models that predict the onset of severe COVID-19 are urgently required to support sensitive decisions regarding patients and their treatments. In this study, we developed a multivariate survival model based on early-stage CT images and other physiological indicators and biomarkers using artificial-intelligence analysis to assess the risk of severe COVID-19 onset. We retrospectively enrolled 338 adult patients admitted to a hospital in China (severity rate, 31.9%; mortality rate, 0.9%). The physiological and pathological characteristics of the patients with severe and non-severe outcomes were compared. Age, body mass index, fever symptoms upon admission, coexisting hypertension, and diabetes were the risk factors for severe progression. Compared with the non-severe group, the severe group demonstrated abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen, and coagulation function at an early stage. In addition, by integrating the intuitive CT images, the multivariable survival model showed significantly improved performance in predicting the onset of severe disease (mean time-dependent area under the curve = 0.880). Multivariate survival models based on early-stage CT images and other physiological indicators and biomarkers have shown high potential for predicting the onset of severe COVID-19.
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Registered trials
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