ArticleScientific reports2025
AI-derived CT biomarker score for robust COVID-19 mortality prediction across multiple waves and regions using machine learning.
Article in Scientific reports, 2025. 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Evidence-Guided Multimodal Risk Prediction Framework for Severe COVID-19 Outcomes Using EHR and CT Imaging for COVID-19 Clinical Decision Support.Bioengineering (Basel, Switzerland) · 2026Article
- Longitudinal modeling of Post-COVID-19 condition over three years: A machine learning approach using clinical, neuropsychological, and fluid markers.Scientific reports · 2026Article
- AI-Based Quantitative HRCT for In-Hospital Adverse Outcomes and Exploratory Assessment of Reinfection in COVID-19.Diagnostics (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
This study aimed to develop a simple, interpretable model using routinely available data for predicting COVID-19 mortality at admission, addressing limitations of complex models, and to provide a statistically robust framework for controlled clinical use, managing model uncertainty for responsible healthcare application. Data from Belgium's first COVID-19 wave (UZ Brussel, n = 252) were used for model development. External validation utilized data from unvaccinated patients during the late second and early third waves (AZ Delta, n = 175). Various machine learning methods were trained and compared for diagnostic performance after data preprocessing and feature selection. The final model, the M3-score, incorporated three features: age, white blood cell (WBC) count, and AI-derived total lung involvement (TOTAL
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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.