ArticleScientific reports2021
Machine learning based predictors for COVID-19 disease severity.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04834934 (Artificial Intelligence - SARS-CoV-2 Risk Evaluation), which is not on this map. Cited by 39 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.
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.
Artificial Intelligence - SARS-CoV-2 Risk Evaluation
Who cites it
39 citing papers in PubMed.
- Machine learning application for the prediction of SARS-CoV-2 infection using blood tests and chest radiograph.Scientific reports · 2021Trial
- Machine Learning-Based Severity Stratification for Smart Preventive Decision Support: Evidence from Measles Surveillance in a Resource-Constrained Region.Journal of clinical medicine · 2026Article
- COVID-19 severity analysis for clinical decision support based on machine learning approach.Scientific reports · 2025Article
- Calculating the Risk of Admission to Intensive Care Units in COVID-19 Patients Using Machine Learning.Journal of clinical medicine · 2025Article
- Classification of the ICU Admission for COVID-19 Patients with Transfer Learning Models Using Chest X-Ray Images.Diagnostics (Basel, Switzerland) · 2025Article
- Machine learning-based model for predicting all-cause mortality in severe pneumonia.BMJ open respiratory research · 2025Article
- Finding Consensus on Trust in AI in Health Care: Recommendations From a Panel of International Experts.Journal of medical Internet research · 2025Article
- Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes.Communications medicine · 2024Article
- Revolutionary Point-of-Care Wearable Diagnostics for Early Disease Detection and Biomarker Discovery through Intelligent Technologies.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Review
- Feature Identification Using Interpretability Machine Learning Predicting Risk Factors for Disease Severity of In-Patients with COVID-19 in South Florida.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Machine Learning-Based Prediction of COVID-19 Prognosis Using Clinical and Hematologic Data.Cureus · 2023Article
- Visual transformer and deep CNN prediction of high-risk COVID-19 infected patients using fusion of CT images and clinical data.BMC medical informatics and decision making · 2023Article
- Application of machine learning models to identify serological predictors of COVID-19 severity and outcomes.Research square · 2023Article
- Early and fair COVID-19 outcome risk assessment using robust feature selection.Scientific reports · 2023Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Machine learning-based mortality prediction models for smoker COVID-19 patients.BMC medical informatics and decision making · 2023Article
- Predicting intensive care need for COVID-19 patients using deep learning on chest radiography.Journal of medical imaging (Bellingham, Wash.) · 2023Article
- Association between SUMF1 polymorphisms and COVID-19 severity.BMC genomic data · 2023Article
- Diagnosis of autism spectrum disorder based on functional brain networks and machine learning.Scientific reports · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predictors of the need for intensive care and mechanical ventilation can help healthcare systems in planning for surge capacity for COVID-19. We used socio-demographic data, clinical data, and blood panel profile data at the time of initial presentation to develop machine learning algorithms for predicting the need for intensive care and mechanical ventilation. Among the algorithms considered, the Random Forest classifier performed the best with [Formula: see text] for predicting ICU need and [Formula: see text] for predicting the need for mechanical ventilation. We also determined the most influential features in making this prediction, and concluded that all three categories of data are important. We determined the relative importance of blood panel profile data and noted that the AUC dropped by 0.12 units when this data was not included, thus indicating that it provided valuable information in predicting disease severity. Finally, we generated RF predictors with a reduced set of five features that retained the performance of the predictors trained on all features. These predictors, which rely only on quantitative data, are less prone to errors and subjectivity.
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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.