ArticleHuman genomics2023
An AI-powered patient triage platform for future viral outbreaks using COVID-19 as a disease model.
Article in Human genomics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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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.
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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
8 citing papers in PubMed, 11 citations in OpenAlex.
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- Healthcare 5.0-Driven Clinical Intelligence: The Learn-Predict-Monitor-Detect-Correct Framework for Systematic Artificial Intelligence Integration in Critical Care.Healthcare (Basel, Switzerland) · 2025Review
- Artificial intelligence in transitional care: practice, promise, and pitfalls-a scoping review.Frontiers in digital health · 2025Review
- Isotopic Ratio Outlier Analysis (IROA) for Quantitative Analysis.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Article
- CYP2E1 in 1,4-dioxane metabolism and liver toxicity: insights from CYP2E1 knockout mice study.Archives of toxicology · 2024Article
- Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.Healthcare (Basel, Switzerland) · 2024Review
Corrections and comments
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Authors and funding
27 authors at 9 institutions in 4 countries.
Funding
Abstract
Over the last century, outbreaks and pandemics have occurred with disturbing regularity, necessitating advance preparation and large-scale, coordinated response. Here, we developed a machine learning predictive model of disease severity and length of hospitalization for COVID-19, which can be utilized as a platform for future unknown viral outbreaks. We combined untargeted metabolomics on plasma data obtained from COVID-19 patients (n = 111) during hospitalization and healthy controls (n = 342), clinical and comorbidity data (n = 508) to build this patient triage platform, which consists of three parts: (i) the clinical decision tree, which amongst other biomarkers showed that patients with increased eosinophils have worse disease prognosis and can serve as a new potential biomarker with high accuracy (AUC = 0.974), (ii) the estimation of patient hospitalization length with ± 5 days error (R
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.